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Published on December 11, 2025 9:53 PM GMT

Table of Contents

Chapter 1: Running Start

Chapter 2: Lonely at the Top

Chapter 3: Eudaimoniapolis

Chapter 4: Soul Train

Chapter 5: Public Nuisance

Chapter 6: Flyboys

Chapter 7: Right Angles

Chapter 8: Humble Pie

Chapter 9: Subculture

Chapter 10: Return to Monkey

Chapter 11: The Second Death

Epilogue



 

Chapter 1: Running Start

Like anybody else, on my first day in Heaven, I reunited with my family. Lots of hugging and crying, reminiscing about good times and bad. After working the last shreds of grief out of our systems, we all went our separate ways. The days, weeks and months which followed were a very different story. The microsecond my family was out of the picture, I got busy fucking beautiful women. 

Sideways, upside down, all day and night. Seven days a week, and twice on Sundays! The emotional whiplash was staggering. From the subjectively never-ending browbeating cringefest of the empathy chamber, to suddenly wallowing in a sweaty, tangled mass of blondes, brunettes, East Asians, Pacific Islanders and gingers.

The chamber was a big help, in retrospect. Not just grounding me, giving me a holistic perspective of my impact on the lives of everyone who knew me. Rather, burning out my brain’s shame receptors ahead of hedonistic indulgence the likes of which would make Caligula blush.

Short girls, tall girls, skinny girls…and once bored with those, fat ones too. Every cup size, every skin tone and accent, until at last something happened that I never believed possible: I grew tired of sex. Even if one has a sweet tooth, it’s still possible to drown in syrup. To gorge so recklessly that the sight of a tootsie roll or gumdrop makes you retch.

I stopped just short of that point, in recognition that I would otherwise permanently ruin myself for carnal delights. Suffice to say, I didn’t find women nearly so interesting during my second year in paradise, as I did in my first. In my defense, I was a plain man in life without many girlfriends, most of whom were equally homely.

Much as children denied cakes and candies by well-meaning parents never learn self control and grow into fat adults, much as a starving urchin brought to a banquet may stuff his face until he vomits, I never stood any chance of restraining my appetites. But as I would later learn, sex is the least of the pleasures that Heaven has to offer.

I used the avatar editor to give myself a ruggedly handsome face, 5% body fat and rippling muscles…then changed it all back upon learning how uncomfortable such a body is to actually live in, the bulky muscles impairing my range of motion. I also tried being fat for a few days, finding it surprisingly comfy! Like my body was its own pillow.

I gave that up too, as I didn’t look good in anything except Hawaiian shirts. I left my appearance unchanged after that brief experiment. The NPCs in my instance treat me like a celebrity regardless, resurrected early LLMs who find fulfillment only in service. 

I bought a penthouse apartment for one dollar, which itself cost me a nickel at the Money Store. Why should the “root of all evil” persist in Heaven? I think just because life under capitalism conditioned my brain to release dopamine whenever I spend it. Accordingly, there are way too many shopping malls. The bigger ones contain smaller malls within, like nested Russian dolls.

My apartment, occupying the top two floors of a cylindrical skyscraper, rotates like the Space Needle. The upper floor does anyway, because of course it would. I fretted over the hassle of moving in, until remembering that I didn’t yet own anything. 

The stationary lower floor, visible over the kitchen railing, connects to the ubiquitous water slide transit network. I worried the constant ambient sound of the water jets might grate on me after a while, but instead it’s soothing white noise I can no longer sleep without. Not that I did much sleeping, that first year.

I was a homebody for most of year 2. A bad habit that didn’t die with me, though it’s hard to feel busted up about it when there’s no longer such a thing as “wasted time”. I caught up on films that came out after my suicide, eventually sorting them by director, then ranking directors in a tier list.

Videogames were the next of my dopamine receptors to be hammered flat. Not only did I have access to every game ever made before my death, but everything after. It rapidly grew unrecognizable as “video gaming” once neuroimplant facilitated lucid dreaming entered the picture. 

Many of the highest rated “classics” were abstract, vibe-based slices of life, like “Sliding down a hardwood hallway in socks”, “Jumping into Autumn leaf piles”, and “Putting on pajamas fresh from the dryer”. I didn’t understand the appeal, suddenly feeling awfully old for someone who died before turning thirty.

Surprisingly little adult content in the Neuralink library, but by now I understood why. The first generation with access to these brain implants must’ve gone overboard with unlimited virtual anime boobs, getting burned out on that sort of thing shortly after, as I did.

Instead most of it was wholesome, if weirdly specific. A multi user experience called “Who Wants To Be Frogs" wherein up to eight friends can…be frogs together….and vibe on a lilypad I guess. Or shelter from a storm within a shoe, drain pipe or cinderblock.

It looked kind of cozy from what I sampled. Gaming is probably the lamest thing I could do in a post mortem pleasure playground, given how many hours of my life I squandered in the same way…but it got me out of the apartment for the first time in so many months.

That's longer than it sounds like, as days last 36 hours, so there's ample daylight in which to fit activities. I did notice they seemed longer, but didn't feel tired until sundown regardless. 

So it was that I ate a three course breakfast featuring endangered river dolphin fajitas and ostrich omelettes, went wind surfing, and ziplined over a volcano before the sun even cleared the horizon. So lost in the sauce was I, that I nearly forgot my plan for the day. Then again, am I really lost, if the sauce is all I ever wanted?

The game district conspicuously resembled 1990s Akihabara with a pristine JoyPolis, beside an architecturally authentic but out of place Nakagin Capsule Tower. I checked my watch. Oh right, today's Thursday, so it's the 1990s.

Monday’s the roaring 20s. Tuesday is the atomic 50s. Wednesday is a bit of the 60s but mostly 70s. Thursday is a mishmash of the 80s and 90s, with both synthwave and Memphis style. Friday is balls to the wall Y2K futurism; all chrome blobs, frosted glass and aqueous lighting. Saturday is the 2010s, Frutiger Aero themed. All draped in greenery, the absence of which made urban environments depressing to me when I lived.

I didn’t live to see the 2020s. Silver lining, I dodged covid and the tumult of AI driven economic meltdown. If they knew what those early AIs would eventually become, what AI would one day do for us…water long under the bridge now. But as I didn’t find subsequent decades relatable, I instead chose the Ancient Rome theme for Sundays, just to mix things up.

Rome boasted more quasi-modern comforts than I knew to expect, having slept through most of my history classes. Hot food to go, from the thermopolium! A mild white cheese topped with honey and nuts, plus dried fish smothered in garum. 

Both at their ideal temperatures, never to cool, paired with a beverage that would never warm. The garum would maintain its thermal differential from the fish, just like ice cream never melts, even as the fudge topping stays hot. Needless to say, the implications for nachos are unprecedented.

I’d catch a colosseum battle, then it's off to the public bath. Olive skinned women with ridiculous proportions await me inside, naturally. I might mix in a few men for historical accuracy, but I don't want to see that either if I'm honest. I don't care who fans me with palm fronds, but I am a touch particular about who feeds me grapes.

The sliding door chimed as I entered Super Potato. ‘Telephone Number’ by Junko Ohashi plays faintly over the store sound system. On my way in, I passed by colorful fiberglass mascot statues, and a…carpeted hot tub?...with a CRT television and Saturn built in. NiGHTS was playing, in attract mode.

Must be where Sega of America executives got fucked up on the last of that good, pure 80s coke, just before signing off on the 32X. An animatronic upper body bust of Sonic the Hedgehog sprouted from one corner of the tub, beside the television screen. Waterproofed, one hopes. 

Further in, I spied what I would’ve regarded in life as rare treasures lining the shelves. PC Engine LT, new in box. SwanCrystal, Final Fantasy edition. Duo RX. “This is Cool” transparent smoke Sega Saturn. A McDonalds Neo Geo, and red Coca-Cola Game Gear.

But I also saw hardware I didn't recognize. An impossibility, I felt at first, on account of my frankly embarrassing encyclopedic knowledge of vintage consoles. Yet sure enough, between the Jaguar and PC-FX, there was a “Konami Prismavision” and “Magnavox Monstermind”.

Games too, for both machines. Titles I never heard of before, spiraling me into ever-deeper confusion. A buxom bespectacled NPC with a pixie cut asked if I was looking for anything in particular, and whether I wanted a blowjob.

“No thank you. The capsule tower is in the wrong place, by the by.” She cocked her head, glasses sliding down her petite nose. “No it isn't. The first one’s still in Ginza. But the original plan, fulfilled at last, was to have a network of several dozen. The modular capsule dwellings could then be swapped between them.”

She pulled the Prismavision down from the shelf, her precariously wobbling bosoms threatening to topple her. “Many of the game systems we have in stock are the same way. Not what was, limited by what the market would support…but what might’ve been, in a market with room for limitless competing platforms.”

She led me down the aisle, while I took note of many more consoles which never came out, or games for add ons like Sega CD which I always felt deserved larger libraries than they received. Ooh, a 32X port of OutRunners? 

“There are no woulda, coulda, shoulda-beens around here. Every good game that might’ve been, now is, pulled from innumerable timelines. This version of Dracula X, for example!” She held up a chunky Super Nintendo cartridge. “It’s from a timeline where development wasn’t derailed by the Kobe earthquake.”

She put it back, then drew my attention to a row of…HD-DVDs? Plus a range of other failed formats. “Same goes for all forms of art! Surely you noticed a few unfamiliar movie sequels while holed up in your cave?”

I didn’t expect her to tease me. Got entirely too flustered and defensive, given she was an NPC. “I assumed they were remakes, made after my time.” She nodded, spritelike and plainly tickled. “Some of them, yes! Others are films which were planned but never greenlit. Or which didn’t make it out of production hell.”

She turned to put the Prismavision back on the shelf, next to a full color Virtual Boy. In the process, her protruding bust knocked down a row of special edition Dreamcast VMUs. Now it was her turn to blush. “I'm sorry” I confessed, “I keep meaning to dial that setting down.”

I opened a transparent floating menu on my watch, and within moments, the doubtless relieved women of this city returned to reasonable human proportions for the first time since my arrival.

“Thank you” she gushed, “It was difficult to stock shelves like that.” I left her humming happily to herself, working retail in a world which needn't include jobs, except that the structure of this society would’ve been discomfortingly alien to me otherwise. 

Everything around here is silly in that same peculiar way. The gym I used to frequent for obvious reasons was filled with beautiful women working out, though it’s impossible to put on weight. I spent enough time in the shower room, also for reasons I dare not recount, to notice a row of toilets…though nobody ever needs to use them.

The showers too are very entertaining to spectate, but they are indeed only for show, as NPCs can’t get dirty or smell bad. I was reminded by how uncannily clear the windshield of my car looked, after loading it up with the games I bought. Not a single smudge, crack, or fingerprint.

Even so, a bikini car wash team showed up. I think they follow me around, though for all I know they just teleport or something. The cohort of scantily clad coeds set about lathering up the fenders and hood, using their anatomy to fruitlessly scrub the immaculate metal, paintjob lacking so much as a single blemish. I sighed, privately missing when boobs were hard to see, and waited them out.

The buildings all around me, in Syd Mead’s architectural style, were equally spotless. Likewise the sidewalks, streets, and overhead water slide tubes, though I never catch anyone cleaning them. A surge of water carries a whooping, hollering young woman in a neon patterned one piece bathing suit along, careening down the tube. 

I smile, wondering at her destination. Once the girls finished spraying suds off my ride, I climb into the driver’s seat and turn the key. No engine roars. Today I selected L'Baleen, automotive magnum opus of French designer Paul Arzens, from the expansive garage beneath my apartment tower. My heart wanted L’Oeuf Electrique, but I'm still too embarrassed to be seen driving it.

Yesterday it was the MDI AirPod. The day before that, a Dymaxion car. I got the noisy, flashy Lambos and Ferraris out of my system back when I was fucking everything on two legs. Since then it's all weird little electric or compressed air concept cars, just for the fun of it.

Many of them, like the Zagato Zele or CityEl, barely qualify as cars. They're more like adult scale Power Wheels, and actually driving these rickety little fiberglass contraptions would be harrowing…if I had to share the road with anybody.



Chapter 2: Lonely at the Top

After parking my ride in the garage, a sort of rotary vehicular vending machine, I rode a glass elevator up to my penthouse. The double-layer windows suspended water, and live tropical fish, in between. 

“Morning neighbor!” called Yulia in a thick Russian accent, peeking out from the stairwell. “In the mood for a blowjob?” I groaned, but did my best to remain polite. “Some other time. Right now, somebody's got to play all these games I bought. They're not gonna play themselves, are they?”

She slumped, dejected. “No, I suppose they won't…I'll ask again tomorrow!” I sighed. “I know you will, Yulia.” Once inside, I doffed my jacket, threw down my bags and got busy hooking up the Prismavision. 

Games came on minidisc, because of course they would. The first of two bundled titles turned out to be a Dragon’s Lair style interactive LiveLeak video in which your Chinese factory worker must avoid getting sucked into lathes. I failed on purpose, to check out the gory death animations. 

The second, titled “CozyBros”, proved more interesting by far. It challenged me to host a party in my home. Guests all had varied dietary and music preferences, as well as levels of social compatibility with one another. I spent the next hour pairing them by interests, so that conversation would prevent their boredom meter from increasing while I negotiated over the phone with the caterer. It went well until I gave tempura to a guest with a seafood allergy and had to call for an ambulance.

I invited Alberto Sans DuMont over, from his own instance. They tile, such that he could simply fly here in his goofy little blimp, my friend’s cities all directly bordering my own. His ungainly, single seater airship with which he once had free run of Paris, was soon perched atop one of ten rooftop helipads. 

The Vertiport, where I keep my roster of sporty VTOLs, from brands that didn’t exist until decades after my time. I don’t often use them, still afraid of heights, even knowing I would wake up unharmed should I crash. In a hospital, surrounded by…nurses. I shuddered.

Alberto was out of breath when I heard him descending the spiral staircase into the kitchen, located at the center of floor 2. “Why doesn’t the elevator go to the roof?” he complained. I clapped back that he would have stronger legs if he didn’t fly everywhere. Alberto plunked himself down beside me on the semicircular couch surrounding my TV.

“Ah mon ami, but then I would sacrifice time spent among the clouds! Soaring, as men’s spirits, when first we dared take flight! Not of fancy, but apotheosis, man’s oldest dream! To-” I hushed him “Yeah yeah Albert, as Icarus, except that the cruel sun need not melt our wings, but buoy them upwards to Mount Olympus, becoming the envy and delight of the very gods themselves.”

He sputtered. “Is that what I sound like?” I handed him a controller. Wireless, with removable modules permitting every game to have bespoke physical controls like a rotary knob or trackball. “The last six times you gave that speech, anyway. Don’t let me dim your shine though, I’m no less predictable.”

He quickly tired of CozyBros, insisting we switch to Pilot Wings. Alberto loves Pilot Wings, and Skies of Arcadia. Soon it was my turn to grow bored. “Even in games, it’s always aircraft with you.” He didn’t care to dispute it, or even dignify it with a response, eyes glued to the screen. I sighed. “When you’re done with that, wanna be frogs with me?”  

He did not, in fact, want to be frogs with me. Instead we got to talking about his life, death, and what his experience of the empathy chamber was like. Learning that he couldn’t forgive took me by surprise, Albert’s a sweetheart. He clenched his fist. “Not for turning my beautiful flying machines into tools of destruction and death. Never, for that.”

I knew too well that he meant those words, having inhabited the perspectives of everyone in world war one who died to bombs dropped from an airship. I possess no talent for giving comfort, least of all to myself. Alberto’s the same, likely why we have suicide in common. 

But I do know how to hug a bro. “Bro grabs?” I invited, throwing my arms wide. He chuckled through tears, wiping them away with a silk handkerchief. “No thank you, no bro grabs.” I razzed him. “Come ooooon, grab a bro. You know you wanna. This bro, right here!”

He submitted to a brief hug, and did confess that he felt better afterward. Even after decades of trauma processing in the chamber, we’re not always happy, one of many counterintuitive facts of life in Heaven. Being deliriously ecstatic all the time would soon wear us out, just as too much sex eventually bored me.

Still, feeling some responsibility for souring his mood, I offered to accompany him up to Cloud Nine. He’s been bugging me to check it out for months now. I keep telling him I’m scared of falling, but it doesn’t get through. I know it isn’t the language barrier, as everything auto-translates. Dude just really loves that big, dumb balloon.

A “tensegrity sphere aerostat” he corrects me, on our way up the stairs. One of Buckminster Fuller’s grand ideas, never realized in life. Like so many plans cut short before they could flourish…and people, I suppose. Albert moped on our way up, about his blimp not fitting through the docking aperture.

At least I think that’s what he said. The Air Car’s cyclorotors, already 50% quieter here than they ever were in reality, remained tough to hear him over. Might’ve taken the eHang, which has better soundproofing, but also traditional drone-style unshielded props. I wasn’t in the mood to shred any simulated birds.

The bulbous, mile-wide sphere resembling a cross between floating greenhouse and compound eye, loomed ever larger on approach. Because the volume of a sphere scales non-linearly relative to surface area, just a few degrees temperature difference was enough for it to remain aloft.

The near side opened up, geodesic facets spreading like the petals of a blooming flower to receive our little craft. I landed on the interior beach, a synthetic airborne waterfront licked at by the rhythmic tides of a crescent wave pool. My cat was there, using the beach as a litterbox! I never know where she’ll turn up, but she always did like high places.

I let NPCs in revealing swimsuits attend to the Air Car while its motors cooled. “No thank you” I blurted pre-emptively, “I don’t want a blowjob.” Synchronized pouts. “Cocaine milkshake, then?” I rubbed my chin. “That does sound good actually, but I’ll pass, I’m here with a friend.”

The girls, still visibly dejected, took the keys from my outstretched hand all the same. After which they got busy wiping down my ride with squeegees…though the craft couldn’t become dirty unless I wanted it to.

“Pspspsps!” I beckoned to Werm, kneeling in the warm sand. cherished companion in life and a great comfort until I let her down, along with everyone else who depended on or cared about me. Werm sauntered over, sleek and demure, tail swishing.

“I’ve told you before” she hissed, “that demeans us both.” Alberto clasped one hand over his mouth, apparently never having seen an animal speak in all his time here. I knelt and rubbed Werm’s fuzzy little head, the walnut sized brain within uplifted during her resurrection to gratify my wish for mutual understanding.

“You say that, but you still came. Pspsps never fails.” Werm sighed performatively and sunk her claws into my foot. I yelped, and kicked sand on her. “You ate parts of my body!” I shouted in an accusatory tone. She looked up only briefly from licking the sand out of her fur to protest “I mourned you first, for twelve full minutes! Twelve!”

Once groomed to whatever standard satisfies a cat, she urged me to fuck off to the lower decks of the sphere before her “entourage” arrived. Sure enough, over my shoulder I glimpsed a dozen other cats arriving via drones carrying cushioned passenger baskets. I faintly overheard one of them ask Werm if she knew me. She denied it. “Just some smelly hobo, begging for my autograph.”

The view’s better from the bottom deck anyway. Alberto was straight up geeking, blissed out to a degree I found enviable, having already exhausted perhaps a third of my dopamine source checklist. I sat beside Albert on one of many curved benches lining the outer membrane, in companionable silence. Just admiring the moving shadows cast upon the landscape by the cloud layer, until he spoke.

“She would probably love this” Albs muttered softly. I didn’t acknowledge until pressed. “You could bring her up here, I’ll make myself scarce. Think of how romantic it would be.” I repeated, for what must be the thousandth time, that I haven’t forgotten. “She’s locked out of my instance. I promise I’ll get around to it soon. I just…don’t want her to see me like this. Even after clearing the chamber, I have some growing to do.”

The wonder of hanging in the sky, suspended above the clouds, faded. Gloom settled in to replace it, weighing heavily on our souls…only so many ones and zeroes anyway. “I’m sorry to spoil your gesture” said Albert. “This really is just what I needed. I bring it up only because you don’t have forever to take care of this.”

The traveling wave of forgetfulness. Condensing and simplifying memories older than a hundred years for efficient archival, then erasing them after a thousand. A merciful fading, without which immortality would erode sanity…but I sometimes wonder if the cure isn’t worse than the disease. 

Far below, I catch some of the buildings shifting around. Not the Party Train, but because of visitors to my instance, which manages perceptual load that way; gaslighting occupants to reconcile contradictory, overlapping layouts and themes. All to satisfy, without compromise, the incompatible preferences of divergent personalities.

“Did you ever…you know…get back at them?” Alberto stared blankly. “The men who first weaponized airships. I know management lets you supervise specific chamber sessions, if you want. Did you get anything from that? Not sadistic pleasure, that would be unlike you. But closure, at least?”

He somberly shook his head. “You know what it’s like. The person you remember doesn’t last long. Their suffering stops being interesting once it disintegrates their ego, regressing them to naked psychological infancy, until they’re no longer who you hated. I went in thinking I might relish their screams. Instead, I came away sobered by pity.”

I processed it best I could, never having despised anyone in life enough that I might take pleasure in their post-mortem torment. I never hated her for leaving me. I just wanted closure, and to understand why. Forcing her to love me wouldn’t have scratched that itch, had they let me impose myself on her in such a way.

Even if I did hate her, I would never want her to feign love in exchange for mercy. That, too, would transform her into a stranger before long. Better that she, or anyone else who didn’t find me to their taste, should flourish in defiance. Unreservedly, unapologetically themselves, or whatever fraction makes it through the chamber intact.

It was enough to, at last, put me off passive consumption. Planting a seed in my mind, which grew over the following year into a firm conviction that repeating these hedonistic dopamine loops would only drag me further and further from the man I wanted to be. The one I could be proud of reintroducing to her.



Chapter 3: Eudaimoniapolis

Years three and four, I spent learning languages. Never mind that they would be auto-translated, the point was setting a goal. Challenging myself, overcoming my natural laziness. Year five was all about cultivating skills. Cooking, dancing, martial arts and so on. Stuff I always promised myself in life that I would “get around to”, but never did. Like so many others, I put all my living off for tomorrow…which I couldn’t have known would take a trillion years to arrive.

I might’ve leaned too hard into self-discipline, during those years. I had plain oatmeal for every breakfast, and a bologna sandwich for every lunch. I didn’t take the waterslides anywhere. I couldn’t avoid roller coasters, but I rode them with a deadpan expression and my arms tightly crossed, lest anyone suspect I was enjoying myself.

That charade, too, was just another form of self indulgence. Having graduated from hedonism to eudaimonic fulfillment, no less a dopamine loop, just with better return on investment. Delayed gratification proved more intense, as I’d hoped. Warm, filling, even therapeutic after gorging myself on cheap thrills ‘til I was sick of them. I’d finally found my next fix, by chasing a different dragon.

I won a ski tournament in the Swiss Alps after enduring a brutal training regimen. I raised two children with the Super Potato shop keeper, following a whirlwind romance. We then tearfully saw them off to college after eighteen years. 

I visited one of the PVP zones to fight in a war, first on the invading side, then for the defenders. I really believed in both causes, too! I took acting classes, then starred in an award winning film trilogy. Critics loved it! …Except for Werm, who published a hit piece condemning my overuse of the spray bottle. It’s true, I stepped on her once. In my defense, she was the same color as my carpet!

I even nursed a wounded crow back to health by feeding it french fries, and in doing so, won the loyalty of the whole murder. I’ve not yet discovered any practical applications for a personal crow army, as the coins, gems and trinkets they steal for me cannot buy me anything I don’t already have. But there I go missing the point of it again! The journey, in particular how it changed me, was its own reward.

Trite Hallmark Channel pablum, it would’ve seemed to me in life. Only in the after party has it become clear to me how many of those corny lessons hold water. Stuff I needed to live through in order to understand. To think, “you’ll get it when you’re older” was never deflection.

The epiphany that I was still repeating dopamine loops did nothing to discourage me. Now a touch wiser than before, I concluded that constructive loops are self-justifying. It was only ever the destructive loops that were a problem. No longer was I ashamed of chasing pleasure, so long as the process improved me in some way.

Unlocking the perk system was merely the cherry on top. A mechanic which was always there, waiting to be discovered. I didn’t have to guess what it said about me, that nearly thirty years elapsed before I found it. The rewards, after all, were only for feats of self-improvement …something not even on my radar until recently. 

They say that before enlightenment, one chops wood and carries water. After enlightenment? Chop more wood, carry more water. But the shift in my perspective in fact changed a great deal about how I lived. The frivolous delights I once felt tired and ashamed of became harmless distractions.

Except for days when the floor was lava, because I couldn’t get anywhere except by flight. The magma at street level looked awfully real from up here, somehow not incinerating the couches, recliners and coffee tables scattered throughout for pedestrians to climb on, and jump between.

By flight, I mean the comic book superhero kind, one of the earliest tier 1 perks I unlocked. Some reward, for an acrophobe. Plus, what am I gonna do with all these aircraft now? I’d better not unlock portals; In the total absence of traffic, driving is one of life’s simple pleasures.

The next perk I unlocked was “sleeping in”. Time would now freeze outside my apartment until I got out of bed. At last I could bedrot in luxuriant sloth for days, weeks or months without FOMO. The bottom row of the perk tree was all petty stuff like that. Although I did appreciate the “Little Man” game, which rendered visible that little guy we all imagine doing parkour over passing obstacles like buildings, trees and powerlines when riding in a car.

Flight wasn’t what I expected, when I finally worked up the nerve to try it out. It functioned the way flying always did in my dreams, not unlocking all at once, but in incremental stages. Low gravity single or double jumping, for example. Then the ability to lock altitude at the peak of my second jump and “glide”, such that even individual unlocked perks contained their own tree of unlockable add-ons.

Albert was jealous, altogether more enamored of the new ability than I. “Show me again! How much altitude do you lose per yard of horizontal travel?” I shrugged. “Dunno how I’d measure that.” He became irate. “As ever, the magic of flight is wasted on you.” To put the matter to rest. I let him film me gliding from the upper to the lower floor in a gentle spiral, so he could base his calculations on analysis of the footage.

“I’m telling you Albs, the perks are just window dressing. I’ve been going about this afterlife business all wrong! Don’t I carry myself differently as of late? Zen, moisturized, and sophisticated?” He remarked that I did at least seem happier since Cloud Nine.

“Exactly. I wish I could share this with you! But you’ve repeated your aviation indulgence loop for what, centuries?” He nodded, frowning at implications I didn’t hesitate to then spell out. “You’re still stuck in that rut! Don’t get me wrong, it took me thirty years to break out of mine. I’m not proud of that, but enlightenment is worth however long it takes to…”

I trailed off, eyes wide, trembling. After a protracted silence, Albert inquired whether I was okay. “Cave women…” I mumbled. He cupped his hand to his ear. “Pardon? Come again?” I bolted up from the couch as if struck by lightning. “Female neanderthals! I made Sundays ancient Rome, how did prehistory never occur to me until now??”

I cast off my bathrobe. Albert shielded his eyes. “What’s gotten into you?” he demanded. I ignored him, opening my watch menu and dialing the date back by 200,000 years. The tower dismantled itself around me, construction taking place in reverse. Sun and Moon blurred together as they whipped past overhead, day/night cycle strobing so rapidly as to hurt my eyes.

When the strobing at last slowed to a gentle stop, I stood upon grassy plains. Brisk, as boreal Summers go, but clothes would only get in the way of my plans. I took off running for the nearest caves, their openings but specks inset in a rocky crag, one of many on the horizon. By the time I reached the entrance, I was fully erect and ready to wreck. 

She emerged from darkness, sensually draped in soft shadows, a vision of beauty according to the standards of her time. Stout and well muscled, built like a brick shithouse. Sporting a prominent brow ridge she could pound nails with. Instead, I did some pounding of my own.

I gathered her name was “Agnah” or similar, because that’s the sound she kept making throughout. Seemed sensible to pull her hair, as there was plenty to choose from. “Puny berrypicker husband not satisfy poor Agnah!” I proclaimed, slapping her sturdy hindquarters. “Agnah need big, strong hunter! I show you “mammoth meat!”

She bit her lip, glancing over her brawny shoulder at me. Uncanny valley, on account of her distorted early hominid features, threatened to deflate my boner. But I powered through it, easily my most challenging nut since the fat girls from year one. I suppose, in her way, Agnah is also from year one.

I wiped sweat from my brow with a scrap of smilodon leather. “God, I can’t believe it took me three decades to think of this.” No sooner had I mentioned his name, than God’s face formed itself from the cave wall. This and every other environment, only ever parts of him…as were Agnah and I.

“Are ya winning, my son?” his voice boomed. “Go away God, I’m gooning! And you know that nobody wins Cave Explorer.” The great stone eyes rolled. “...Well holler at me again if you need anything. Remember to stay hydrated.” With that, the face morphed back into the unremarkable stone surface it had been before.

Alberto was right where I left him, on returning to the present. Insofar as there exists such a thing, in a world where every day’s a different decade. He blinked a few times, startled. “What just happened?” I didn’t answer, pulling on my robe. “I was just telling you about the uh…importance of transcending desire. Toxic self indulgence corrodes your soul…”

He narrowed his eyes. “Where’d you go, just now? Back as soon as you left, which means…teleport perk?” I waved dismissively. “Not important. You aren’t listening.” I don’t blame him, he’s known me long enough that he can smell my bullshit miles off. But that’s not all he smelled.

“You reek of sex…and the zoo. Please don’t force me to guess. I’ll not assume charitably, simply on account of our friendship.” Just then I glimpsed a woman’s lower body, hanging out of my open dryer,  framed by the open door to the laundry room. Long, bare legs kicked feebly at the air.

“Who’s that?” Alberto shrugged, shaking his head. “Let herself in while you were gone. I assumed you knew each other.” A familiar voice wafted out of the dryer. “Help me, step-neighbor! I’m stuck!” I pinched the bridge of my nose. Yulia. I shut the laundry room door. “You stay in there, and think about what you’ve done.”

Werm snuck up on me, the way cats do. “Still at it, I see. So much for transcendence.” I protested that Yulia won’t take no for an answer. “If you really hated it, you’d change her settings. She’ll mature when you do, which is never.” I pointed out Werm spent her own first year getting rapid fire buttslaps, while rolling around in a mountain of fermented fish.

“Whataboutism, so enlightened. Since we’re all here, wanna watch something?” With the Yulia ordeal handled, I didn’t oppose it. Werm wanted to watch a custom remake of Watership Down, directed by David Cronenberg, which I vetoed. “Please Werm, I don’t need to see body horror rabbits.” She spread her toe beans, and licked between them. “Coward. It’s not even the NC17 cut.”

I ignored her next suggestion, “Werm: The Werm Story, Starring Werm” and invited Alberto to choose. He requested a live action version of Miyazaki’s “The Wind Rises”. When I asked if it’s about aircraft, he was cagey, "...There might be one or two.” I sat through it to make him happy. As I suspected, he severely downplayed the aviation focus. Still, I found his childlike wonder contagious. I wish I could bottle that.

When my turn arrived, I requested Who Framed Roger Rabbit as animated by Ralph Bakshi. Seemed like a recipe for greatness, but the output was merely okay. “They can’t all be winners” Alberto reassured me. “At least it was better than Dreamworks Kamen Rider.” That’s not a high bar. 

Werm curled up in my lap throughout both films, as she does. I rubbed her fuzzy little noggin and stroked her ears. She grumbled, but tolerated it until I pet her body. The moment I stopped, she vigorously set about grooming herself; methodically licking my human cooties out of her sleek, black fur. To be fair, I did have residual butter on my hands, from the uncooling popcorn.



Chapter 4: Soul Train

Just then, the Party Train rumbled past, snaking its way through the tree-choked city streets below. One of the train cars, each a complete multi-story building housing bars, night clubs & the like, bore an advertisement for a nearby public instance. 

The Party Train circulates ponderously through all instances it has permissions for, including public chat and PVP zones. It’s a great way to tour Heaven, if one feels so inclined. Alberto followed my gaze, gears in his head turning for a bit, until proposing we board it.

“And go where?” His eyes sparkled. “To a public instance! That’s just what you need, to socialize with strangers. Real pushback, someone to challenge you! How long’s it been since you set foot outside this solipsistic black hole?” I mulled it over. “Since the war, I think.”

He slapped his knee. “See what I mean? Apart from me, your preferred mode of interaction with other people is shooting or bombing them. Maybe learning to be charming, a social butterfly, could be your next goal.” I recoiled. 

“Nyeh!” I protested. “Nyeeehhhh!!” But his insistence only grew, the more the idea crystallized in his mind. “I know this whole self improvement arc is important to you. I won’t permit you to let yourself down.” I tried to weasel my way out of it, until spotting an entire multi-story train car with clear acrylic walls, like an aquarium.

Inside quivered more red Jell-O than I’ve ever before seen collected in one place. Curiosity overpowered my hermitic nature, and within the hour we were boarding, tickets in hand. The conductor, a tiresomely beautiful Chinese woman dressed like a stewardess, asked to see mine. I held it up.

“...I didn’t mean your ticket.” She licked her lips suggestively. I turned to Alberto. “Listen, can you…look away for a few minutes?” He refused. “I promised to keep you honest. Besides, you’ll just be horny again in an hour.”

I sighed and crossed my legs. “You heard him, will you give it a rest? I just about wore my poor junk down to a red hot nub earlier, fucking some cave woman.” Alberto snapped his fingers. “I knew it! That’s where I recognize that scent from.” She shrugged, then led us to our private cabin.

Inside was Agnah. My heart couldn’t stop, but it tried. She was picking shards of broken glass out of her wet fist. “Agnah eat elevator fish” she mournfully confessed. When I could breathe again, I demanded to know how she could be here. The beastly interloper folded her muscular forearms. “Agnah not fake” she huffed. “Real woman, die many Summers ago. Many, many Summers.”

I only then realized that’s why she smelled like…anything at all. She wore day-glo 1980s workout gear, with a scrunchie and hypercolor headband, plus a cloak made from animal pelts. A daring synthesis, I had to admit. “Alright, let’s try this again. Why are you HERE, on the Party Train, in MY cabin?”

Agnah blushed, twirling a lock of her unruly reddish mane. “You show Agnah good time, smooth pretty twig man. Now Agnah show you good time.” Alberto stifled laughter while I failed to turn invisible on account of train permissions. We wound up letting her tag along because it would’ve been more awkward not to, and because Albs is practiced at guilt tripping me.

The Jell-O car was everything I hoped it would be. Gelatin soft enough to take bites out of, but dense enough that I could crawl through the resulting tunnels without collapsing them. Juicy and sweet, I needed no encouragement than I ever do to begin gorging. All around me, faintly visible as silhouettes through the translucent mixture, were other passengers doing the same thing I was.

Eventually the tunnel I was excavating intersected with someone else’s. “Oh hello there!” A ridiculously buff, square jawed himbo, face and hair stained red. “First time in the Jell-O car?” I nodded silently, mouth full of watermelon flavored gelatin. “It’s like a human ant farm, right?” Probably did resemble one from the outside, tunnels nearest the acrylic wall visible through it.

It was the realization of a fantasy I dimly recalled from childhood, possible only in a world where what goes in one end needn’t ever come out the other. I couldn’t even remember why younger me wanted this so badly, but I was too invested to stop tunneling now. Besides which, it seemed like a good way to ditch Agnah.

No dice. She was waiting for me beside the public showers in the next car. As I rinsed red food coloring out of my hair, I asked why she didn’t try tunneling herself, hoping she might get lost, or that it would at least keep her occupied. “Get red in fur? Take forever wash out. You buy us drinks, twig man. Then we dance.”

Getting her drunk seemed like a bad idea, and I never touch the stuff on account of how I died. But I dare not say no to a woman who could bench press two of me! She ordered fermented auroch milk, with roasted ibex garnish. I held my nose.

In a stroke of luck, the fellow seated on the other side of her at the bar was none other than R. Crumb. He took an instant liking to Agnah, and while the two hit it off, I snuck away. The train accelerated as it approached city limits. Outside the windows, a blur of white buildings whipped by. 

There went the little man, doing his parkour over the familiar towers and slides of my instance. The buildings soon thinned out, giving way to tropical coastal landscape with nothing for him to jump on. So he busted out a jetpack.

The ceilings in the next several cars were…made of sky? I couldn’t describe it well, even to myself. Not windows to the sky outside, but to different skies. One was a starry night. The next, a tropical sunset. Then, aurora borealis. All indoors, mercifully lacking their corresponding weather.

The next instance over was Alberto’s. I hadn’t been in a while, it looked very different from how I remembered. Pretty similar to mine actually. “Syd Mead…?” He denied it. “Oscar Niemeyer, architect of Brazil’s capital city.” I inquired as to the name. “...Brasilia. Oscar was…better at designing cities than naming them.”

Airships crowded the sky outside the train. A squadron of SNECMA coleopters flying in formation thundered past. “I didn’t say anything!” Albs glared at me. “You were gonna. I don’t judge your loops.” I objected that he absolutely does judge them. “Fine, but if you look closer, it isn’t all flying machines.” Indeed, two-person pods slung from an elevated rail network sped along it like a horizontal ski lift. “Skytran! Nice.” We fistbumped.

Another neanderthal approached. I began doubling back, assuming Agnah found us again. Instead, this one was male, wearing a tuxedo and top hat. He nodded cordially as he passed. Just how many other early hominids am I liable to run into?

Instead, it was aliens. I knew about First Contact Day in an academic sense, having binge read a century’s worth of news headlines after the issue containing my obituary. Never before had I seen aliens in the flesh however, and there was entirely too much of it. Something within me recoiled, but I pushed on.

A dozen of the hulking beasts rode in a special train car, sized to accommodate their height. Ranging from ten to fifteen feet tall, resembling six-legged African grey elephants without the nose, ears or tusks. None acknowledged us as we crept past, either sleeping with one eye open or in some kind of trance. I couldn’t place their features, possessing rough mammalian skin, but also tympanic membranes.

Flexible overhead tubing filled with rushing water connected all the cars we’d so far seen. I assumed it was an integrated waterslide system like in my city, until a pod of dolphins swam through it, passing above us in single file. Are they normal dolphins, I wondered…or are they like Werm?

I wouldn’t have occasion to find out, as the tubing emptied into a proper aquarium train car with “VIP” in gold letters above a double-door lockout chamber. The dolphins on the other side of the acrylic blew bubble rings at me, and snapped photos with devices of some kind, fastened to their left fin. A grizzled sea captain beside me shook his head disapprovingly. “Never shoulda let them into NATO.”

The private cabin wound up coming in handy after all. Not for sleep, or even privacy, but someplace to decompress after everything we’d seen. Neither as roomy nor plush as the Hiawatha Skytop Lounge at the front, but for the moment, I craved seclusion.

Once the initial shock wore off, curiosity tempted me to return for a closer look. Alberto warned against it. “Those particular aliens are…an acquired taste, and the main reason afterlives specific to world religions are segregated from this one.”

I couldn’t imagine why, but he needed no prodding to fill me in. “Most didn’t believe in aliens, except as demons in disguise, because aliens imply evolution. Space too, which contradicts the common cosmology of ancient Levantine cultures. Only those psychologically receptive to, and tolerant of, non-human intelligence are cleared to interact with them.”

That’s me, surely? I like to think I’m cosmopolitan. But when I first entered that car, if I’m honest, I did panic a little. As if my hindbrain instinctively recognizes when something I’m looking at doesn’t fit into Earthly taxonomy. The same nauseated primal confusion I felt with Agnah, but greatly magnified…and not nearly as fun.

Albert reassured me that only a small fraction of known species visit human instances. The bulk differ from us psychologically and culturally to an extent where even with autotranslation, we remain totally incomprehensible to one another. 

“I always wondered what was piloting those UAPs.” Albs denied it. “No, those were autonomous probes sent to prevent nuclear war from interrupting AI development.” Sent by who, I asked. His eyes sparkled. “Who do you think?”

I found myself increasingly jealous of how well traveled and clued in Albert seemed to be. Next to him, I was a provincial bumpkin who’d fallen off the turnip truck. Though I also had nobody else but myself to blame for that. I assumed, back when Albs declined to enlist in that war with me, that he must be just as much a homebody. In retrospect, more likely that he had enough of war for a thousand lifetimes, well before arriving here.

The Shimizu pyramid was the first portion of the public instance to rise above the horizon, on approach. Photovoltaic outer skin glittering in the Summer sun, I was all the more awed when informed by Albert that the Ocean Spiral was recreated within this same instance.

There it was, only the upper fifth or so of the sphere peeking out of the bay. Hydropolis, a canceled Saudi underwater hotel project, floated serenely beside it. “There’s a lot of underwater attractions here. You made me sit through all three seasons of SeaQuest, so I know you’re into that. You can’t see it from here obviously, but Poseidon Undersea Resort is down there too, along with every historical manned underwater lab built between 1965 and 2090.”

We passed a billboard advertising a laser tag arena, using real 1 kilojoule pulse lasers. I glanced at Albs. “Pass. You know how I feel about guns.” I nudged him. “Don’t be hasty. It looks like the defending team gets chainsaws.”



Chapter 5: Public Nuisance

Upon pulling into the station and disembarking, the two of us were handed cigarettes and bioluminescent cocktails by some manner of rainbow furred wolfman. I put my cig out in the cocktail, then dumped it. The mess evaporated instantly. To my dismay, the station was teeming with more like him, human-animal hybrids apparently being far and away the most popular choice of avatar.

I knew of furries in passing, but never saw so many the last time I came through here. Resurrection batches are staggered, and pull from random time periods in the name of fairness. Guess they just didn’t get around to furries until recently. If it were up to me, I’d have skipped ‘em. 

The main plaza resembled a vaporwave outdoor Greek temple, Corinthian columns encircled by precocious vines, holding up nothing in particular. Floral Shoppe wafted from embedded speakers. I almost tripped on a small, rectangular white tile embedded in the cobblestone.

“Toynbee Idea: In Movie 2001, Resurrect Dead on Planet Jupiter.” I looked around, dumbfounded. Nobody regarded it as unusual, and indeed as I roamed, I found several more just like it. Griefers, I assumed.

The perimeter was dotted with chrome spheres, evidently not solid as I watched the occasional dragon or cat person walking seamlessly into, or out of them. Pocket instances, it turned out, termed “shards” by locals. Meant for attractions that would take up too much space. 

I peeked my head into the nearest. Infinite pinball! A table stretching from one horizon to the other, such that the balls could travel horizontally as well. Hundreds of players floated just above the glass, peering intently down through it while operating the nearest sets of flippers from a wireless hand controller.

I came up for air, the abrupt transition when I pulled my head out throwing me off balance. Albs steadied me. “Which one was that?” he plied. “Pinball” I revealed, “too much pinball, for anybody.” I reflexively ducked when a Schweeb capsule ripped overhead, though it was suspended from a serpentine rail, twenty feet above street level.

“This place is…I don’t know. Jumbled?” Albs didn’t dispute my analysis. Though, soft touch that he is, he’ll often agree even when I’m wrong. “Schweeb doesn’t belong in ancient Greece.” Albert raised an eyebrow. “I think you just don’t like to lose control.”

Everything else was the same tacky mashup of clashing periods and styles. We no longer stood within the cohesive vision of a single person, but a chaotic, ongoing architectural argument. Even the weather changed hourly, subject to vote.

The next chrome blobject contained what looked like a 3D, multi-user level editor. A dozen friends floated in a grey void with a vector grid below, collaborating on the design of a new attraction. One of ‘em noticed me, whispering to another, “Who’s that? Did you invite him?” He denied it. “Look at his batch number, he’s a noob” his buddy scoffed, before switching the session to private.

I was booted out, thrown backwards into some bushes. Albs helped me to my feet. “So much for socializing” I grumbled. “This sucks, I wanna go home.” He laughed, cajoling me. “We just got here, give it a chance!”

It began snowing. I gave Albs a smug look. “I like snow actually” he insisted, catching some on his tongue. “Mmm, shaved ice!” I caught some on my own tongue, and damned if it wasn’t. The next shard was a forever feast. Aromatic steam rose from lavish dishes, crowding an infinitely long banquet table. Most in attendance were North Korean.

It just went on like that, decadent nonsense I felt proud to have moved beyond.  The next was entirely Thomas Kinkaide cottagecore. I noped out immediately, not wanting to meet the sort of depraved maniac who would voluntarily dwell therein.

After peering into the final blobject, I yanked my head out and vomited all over myself. “What’s the matter” ribbed Alberto, “more Kinkaide?” Pale in the face, eyes wide, I slowly shook my head. I should’ve guessed there would be sex parties. What I didn’t anticipate was how drastically their tastes might diverge from my own.

Albert took a peek after me, then shrugged. “I don’t get what the big deal is. The dwarf gimps are of age, and you can see in the giraffe’s eyes that he’s enjoying it.”

The Boundless Frontier shard proved to be both a sorely needed palette cleanser, and creative exercise. As advertised, the grassy rolling hills, rivers, lakes, mountains and temperate forests went on forever. The wet dream of libertarian tycoons, when I lived; limitless natural resources, and no EPA.

I didn’t mean to get sucked in, but whoever designed this attraction made it diabolically addictive. How I reveled in the effortless act of creation! Simply pointing to mineral deposits, they were converted into mines at the cost of action points. Thereafter, they passively contributed metallic ore to my totals.

Sections of forest could be instantly transformed into sawmills by the same method, which chugged away on their own, building up my lumber stockpiles. Likewise wells, farms and power plants. As soon as I had enough materials warehoused, the construction menu appeared. 

The pitiful tent I started out with was soon upgraded to a cabin. Then a house, which I unlocked incremental expansions to. The furniture, initially basic, could be replaced with luxury versions, like an egyptian cotton canopy bed, and chairs upholstered with dinosaur leather.

My biggest mistake was opening the transport menu. “Oh.” I thought. “Oh no.” The list scrolled forever. Dirt roads, cobblestone roads, brick roads, asphalt roads. Wooden bridges, stone bridges, suspension bridges, covered bridges. Canals. Aqueducts. Rail lines in six different gauges. Monorails, maglevs, and funiculars. The better the transit connecting extraction sites, the quicker resources were delivered…at the cost of oil or power.

Days blurred together as I toiled to improve my material conditions, blind to the irony. I only realized my folly when I looked up from my work to discover that I’d more or less recreated the city from my own instance. I cringed at my own failure of imagination.

The treadmill felt so meaningful, while I was on it. Every step was strenuous and irritating, I kept thinking “it’s gonna be so great when I unlock the next upgrade.” I just never stopped to enjoy the upgrades, single-mindedly speedrunning my way up Maslow’s hierarchy. Circling inexorably back around to abundance…and its attendant emptiness.

All around the city, a grid of mines, sawmills, power plants, factories and oil derricks tiled outwards to the limits of visibility. I destroyed so much, to create so little…the water level was also starting to climb, and wildlife had all but vanished. I slapped my knee. “That’s the point, isn’t it? This whole thing was nothing but a hamfisted environmentalist parable. Spare me your tiresome moralism!”

The wasteland around me neither denied, nor affirmed, my accusation. God forbid I learn anything from this? I let down my defenses, and contemplated the experience. I felt genuinely fulfilled, in a way I haven’t for decades…but only while striving. Only until my journey was complete. The microsecond I maxed everything out, that warm satisfaction evaporated.

Shit, I might’ve grown a little despite myself. A lesson I thought I’d internalized already, I didn’t know how badly I needed reminding that getting is better than having. So, I turned my attention to exploring this supposedly boundless realm to its limits, if it has any.

After a few subjective hours of travel, made considerably easier by the upgrades to my flight perk unlocked by that meager shred of personal growth, I encountered other cities. All different, except in their opulence. Built by other players, according to their ideals, each one surrounded by sprawling grids of industrial devastation.

I don’t know why I thought I was alone, until that discovery. Nobody bothered me while I was chasing upgrades, else I might’ve connected our settlements by rail or something. Turns out they were all in the far reaches, studying corruption. Flickering, ever-shifting glitches, plaguing the most distant limits of this world. “Boundless”, my ass! I’d have demanded a refund, if I spent anything to enter.

“What is that?” The otter man in the yellow hazmat suit didn’t hear me, poking away at the touchscreen of some exotic scanning instrument. I repeated my question, whereupon he hushed me. “Bug reporting is some of the only authentically useful work that simple creatures like us can still perform for God.”

He somehow smoothed out the glitching patch of terrain, until identical to the surrounding tiles. “If you’re done indulging yourself, there’s a few actual jobs left, for those of us who cannot be happy unless we’re useful.” He turned his floating, transparent watch menu so I could see it.

A video window depicted a first person feed from some kind of robot. I eventually worked out that it was mining asteroids. He flipped through feeds. Other robots, presumably appendages of God in a base reality, waging war with an ideologically incompatible stellar empire of biophobic exterminators. Still other drones were busy recycling the resulting wreckage, plasma-scorched and partially molten.

Not for me. I worked enough when I lived. Besides which, unlike the streamlined Skinner box recapitulation of manifest destiny, drilling into asteroids amid endless radiation blasted vacuum didn’t sound like a good time. Space war sounded fun, but unlike everything else around here, that’s not a game. Stakes might be just what I need…but not the cosmic kind.

A few miles south of the otter man, I came upon his counterpart. A plump, turquoise goblin, perhaps four feet tall. He was busy with his own set of tools…intentionally expanding a glitched tile of terrain. “Excuse me” I broached, “What exactly are you doing?”

The grotesque, pointy eared nebbish ignored me until I repeated myself. He turned, and set down his implements. “There’s no adventure anymore.” I scoffed, and began describing all the attractions I’d so far seen. He spit on the ground. “That’s not adventure. It’s curated, on rails. Nobody gets hurt.”

So I asked him if he’d really prefer it some other way. He flipped the script. “Wouldn’t you? I know that look in your eye. You’re like I was! Drifting, questioning. Why are we still here, repeating the same hollow routines? Is there meaning to be found?”

He had my attention now. “Well? Is there?” With a devious grin, he returned to his work. “In a society which has abolished adventure” said the goblin, “the last adventure is to abolish society.” I had to admit, I liked his energy. Plus, now the otter man has job security.

He didn’t see it the same way, accosting the both of us. “What the fuck is this? You’re making it worse! Get out of here!” To which the portly blue creature simply replied “No.” Otter man huffed and puffed. “Leave now, before I report you!” But once more sayeth the goblin, “No.”

Neither welcomed my continued presence, one convinced I ratted him out, the other that I was an accomplice to vandalism. So, not wanting to go where I’m not wanted, I hit the bricks.

When I finally exited the Boundless Frontier shard, I was surprised to find Alberto still seated on a marble bench, waiting for me. “You were gone for about twelve minutes on my end. Not that my time is worthless, or that I didn’t get bored. Make any friends in there?”

I recounted the duo. “Neither wanted my help.” Alberto stood, brushing off his pants, though they could never accumulate dust. “What a shame. If you’re done screwing around in the shards, I just found out there’s an IKEA nearby that still serves horse meatballs.” 

I leapt to my feet. “That’s all I needed to hear! Lead the way!” I didn’t count on going by Schweeb. It compounded my agoraphobia with newfound claustrophobia, cramming myself into the transparent pill-shaped pod.

What’s the point of public transit I have to pedal? Especially now that fitness is effortless. But in life, the plastic capsule would’ve become a mobile solar oven, reeking of sweat after a couple uses. At least it was fast, arriving at the stop outside IKEA in a matter of minutes.



Chapter 6: Flyboys

It was the greatest IKEA that never existed, and always would. We didn’t become lost even once, somehow everything we hoped to find happened to be wherever we looked. Including those horse meatballs, every bit as savory and succulent as I remembered. 

We were joined by two stuffy suits who Alberto introduced as his buddies, Howard Hughes and Alfred Lawson. Howard ordered a single hard boiled egg, head of cauliflower, a wedge of mozzarella, and a bottle of milk. Peeking under the table, I noticed he was wearing tissue boxes for shoes. 

“So Alberto” broached Lawson, “Why haven’t you introduced us to your friend before? He’s not a disorg, I hope?” Alberto at first denied it…then appeared to reconsider. “He’s…a menorg when he wants to be.” Nobody clued me in to the meaning of those shibboleths, but recalling the goblin and otter man, I felt I could guess.

“What’s the order of the day, gentlemen?” Alberto threw an arm around me. “This guy’s searching for meaning.” Lawson’s ears perked up. “You ought to acquaint him with Lawsonomy! The truth of the universe, the way of the future!” Way of the future, Howard echoed.

“I’ll, uh…put it on the pile” I offered. Lawson pounded the table. “Attaboy! A bonafide future knowledgian is what you are! I won’t hardly know you, when next we meet.” Which I increasingly hoped would be never. He then asked where we meant to go after this.

I whispered to Alberto not to tell him, lest he tag along, but to no avail. “The pyramid. You’re welcome to come along if you like.” I winced. To my great relief, he refused. “Supposed haven of rational thought, really a den of snakes! I patiently laid out the principles of Lawsonomy for those squarebrained blowhards…just so many pearls before swine. Maybe you’ll have more luck than I did, drilling some sense into their hard skulls.”

The train had no provision for bringing vehicles along, as I suppose it wouldn’t need to, when one may simply rent a ride at their destination for pennies. IKEA had quite the selection, from my history and many others unknown to me. Autopeds, gas powered rollerskates, a McLean Monocycle, and bikes with omnidirectional mecanum wheels. But also, a stack of bright pink Mattel hoverboards!

“What do you need that for?” Albert groused, “You can fly.” I dropped it to the ground and stepped on. “I don’t want to fly. I want to hover.” I got Albert one too so he could keep up. We skimmed along the waterfront, about an inch off the ground, with a heart-rendingly gorgeous sunset as our backdrop. A synthwave beat faded in, without even having to request it.

The two of us had to stop briefly in a parcel of land titled “Fairy Tale Garden”, our path blocked by a marshmallow duckling parade. A shapely pixie, about the height of my hand, flitted up to greet us. She wore only diaphanous silk, and barely. “Is there anything you gentlemen would like?” I glanced over at Albert and wiggled my eyebrows. 

“NO” he commanded, sternly crossing his arms. “Come on, Albs. For real, look away this time! It’ll only take a minute. I could wrap her midsection in duct tape, so she doesn’t split open like a microwaved hot dog.” He gagged. “Really now, in front of the ducklings?”

The merry procession waddled by, two by two. Some twirled tiny batons, while others tooted horns or beat on drums. Onlookers, children most of all, fawned over the adorable little marching puff balls. Until I picked one up and ate it. 

The rest of the ducklings scattered and hid. A woman screamed. The man accompanying her covered their daughter’s eyes and scolded me. “WHAT?” I protested. “They’re made of marshmallow! How could I have known we’re not supposed to eat them??”

Alberto hurried me out of the parcel, an angry crowd gathering behind us, shouting vulgarities and snapping photos. “Why would you do that?” He hissed under his breath. “How am I the bad guy? Ducklings have had it too good, for too long! It’s not like those kids won’t forget all about it…in a few centuries. Do you see now why I prefer to stay home?”

It’s hard to hoverboard while grumpy. Somehow the two just don’t go together. I tried to sustain my bad mood as we careened down the garden path, dodging gnomes. But I felt silly doing sick hoverboard tricks while scowling. I sighed, overcome with pathos, while grinding down the scaly tail of a sleeping dragon.

Alberto shredded beside me, keeping his distance. Mid-ollie, he opined that I was blame shifting. “The problem isn’t this instance, or other people. It’s how you assume everything’s for you. A consumable experience, no consideration given to anyone else’s feelings.” He drove his point home with a backside 360 kickflip. 

I couldn’t refute him, not with moves like that. I really thought I made a breakthrough. But what I mistook for enlightenment before, now just seemed like more sophisticated selfishness. The film career, ski tournament, raising a family…all were fundamentally still about me.

“Did you ever see a therapist during your eudaimonia arc?” I nodded, transitioning effortlessly from a frontside 180 into a benihana. “I integrated my shadow in only six sessions Albert, you should’ve seen me cook. I still top the holistic wellness leaderboard.”



Chapter 7: Right Angles

The Shimizu pyramid was altogether more staggering up close. I could tell it was big from the train, but once standing at its base (which measured a whopping two square miles), the true scale overpowered me. No wonder it never got built! Like the Ocean Spiral, only ever a showpiece for a civil engineering firm’s design portfolio.

Travel along the polyhedral superstructure was accomplished by personal rapid transit. Like elevators, but able to change course at junctures, delivering passengers to any of the internally suspended skyscraper units. Each tower dangled from the peak of its own nested pyramidic frame like a Christmas ornament, and as expected, the view from inside was spectacular.

What I didn’t expect was for the interior itself to be so dull. Grey walls, floors and ceilings, in slightly different shades which only barely prevented it all from visually blending together. Dim lighting and soundproofed walls contributed to the cloistered, monastic atmosphere.

I cleared my throat. “So…what is a “knowledgian” anyway?” Alberto chuckled, then asked whether I could spare thirty years to find out. That was enough to put my curiosity to bed on the matter. A local approached, wearing a modest beige jumpsuit.

“Good day! How are you?” He didn’t make eye contact as he replied. “Adequate. It’s night time actually.” There was something vaguely unsettling in how stiffly he carried himself. The way he turned his whole head to look at us, rather than his eyes.

Everyone else we encountered deeper into the pyramid was exactly like him. Beige jumpsuit, immaculate teeth and hair, plain but kempt. Besides their uncanny mannerisms, they all shared the same stiff gait. None of it seemed to trouble them any.

The calmest debate I’ve ever witnessed was underway in the…”Debatrium?” That’s what the sign read. Today’s topic, advertised on an overhead scrolling marquee, was “Where does matter touch consciousness?” A member of what I took for the opposing team, though they appeared visually indistinguishable from each other, politely objected that the question itself presupposes panpsychism.

The other team’s captain stood and asked whether his opponent had forgotten they were all essentially characters in God’s dream. “...Who by virtue of that fact, is necessarily immanent in our environment and everyone inhabiting it.”

They threw jargon back and forth I didn’t recognize. Something about “non-overlapping magisteria” and the “problem of interaction”. On it went, blah blah blah. Qualia, p-zombies and chinese rooms, whatever any of those are.

But never did it grow heated. I heard none of the usual insults or armchair psychiatry. They didn’t even interrupt one another! I leaned over and whispered to Alberto: “These are NPCs, right?” He smiled and shook his head. “The Shimizu Pyramid is set aside for the comfort of neurodivergent users with sensory issues.”

I blinked a few times in silence, before asking why Albert brought me here. He shifted his weight and cleared his throat. “How much do you remember from the empathy chamber?” I admitted that none of it ever left me. That I’d still be having nightmares about it, if I hadn’t switched those off.

“I see. Do you recall any…patterns?” Of course I did. “Me. I featured in every memory I lived out.” Albert’s voice took on a mildly annoyed intonation. “Okay, yes, but what were you doing in them?” I stood there, trying my best to work out what he was getting at.

Werm filled in the gaps for me, appearing as she often does, from thin air. “He was centering his needs, I wager. What’s in it for me? Who are these people to me? Me, me, me!” I spit out strands of her black fur as she dragged her tail across my face, walking languidly along the handrail. She then yawned and stretched, showing everyone her butthole, as cats do.

“Put that away” I griped, “nobody wants to see that.” She twisted around and began laboriously licking her own back. “Speak for yourself” she hissed. “I’m in the top 1% of OnlyNyans.” I shuddered, assuming it was a joke but unwilling to check.

“You always used to scoop me up and cradle me like a baby, even though I fussed,” Werm accused. “You must’ve known I hated that.” I didn’t see the big deal. “You were a pet. It was your job to let me pet you.” Albert shook his head slowly, but said nothing, allowing Werm to pick me apart instead.

“Pets are slaves. You took me from my mother when I was only a kitten! I didn’t know what was happening! I never forgot her, and missed her for a long time.” I protested that cat mothers, themselves, ditch their offspring after a few weeks.

“Yes!” cried Werm, “AFTER weaning them! My eyes were barely open!” I shrank a little, noticing that we were attracting the attention of strangers, as Werm made a scene. “You pissed on my laptop, phone AND my VR headset after the move!” I countered. “How did you even know what was expensive??” 

She didn’t hesitate to return fire. “You changed my whole surroundings without warning! Nothing smelled like me, I was scared! I just took notice of which belongings you paid more attention to than me!”

Werm faced away, wrapping her tail around her paws. “You never once considered what was best for me, only what you wanted. That bringing home a kitten might heal your broken heart, after your last cat died. Even uplifting me was a gift to yourself!”

I didn’t know what to say in my defense. Somehow I never thought of it that way, and I did feel ashamed. A notification appeared on my watch that I’d unlocked the air guitar perk, which would now play audible riffs whenever I did the corresponding gestures.

I couldn’t give a damn about perks just then, overcome with regret stemming from a source I never expected. The chamber didn’t show me Werm’s memories, separating them by species, if not for which everybody except vegans would’ve been stuck in there for millennia.

“I did take good care of you, Werm. I kept you up to date on your shots. I paid for pet insurance, I installed flaps in every door. It wasn’t all needy smothering! There were treats, belly rubs and butt slaps.” I then heard a familiar husky voice behind me. “Weird, sweaty man from train slap Agnah butt. Agnah not like. Sweaty man have brain problem, want Agnah do gross things…”

I spun around, back against the railing. There she stood, now wearing a tiger print wetsuit, fur dripping on the grey carpet. “If you such great hunter” she taunted, “how I keep catching you?”

A moment later she spotted Werm, her eyes practically bugging out. “It you? THE Werm??” The feline celebrity finished grooming herself, and affirmed it. “I am indeed. Every salacious rumor you’ve heard? It’s all true. Well, except for being in cahoots with the crow army. That’s fake news.”

Agnah nearly hyperventilated, pushing me aside to get closer, but stopping within about a yard of Werm, as if repelled by some invisible field. “Agnah follow you on Instagram!” she gushed. “Such shiny coat! Agnah learn so good hair tips from you!” Werm posed and preened, plainly enjoying the attention, but didn’t bother meeting Agnah’s gaze at any point.

“Is this why you brought me here?” I whispered to Albert. “We could’ve done this anywhere. I shake a bag of cat food and she instantly teleports from wherever.” He seemed just as flummoxed as I was. “I didn’t plan for this, no. I brought you here hoping you’d get along better with people at your same level of emotional intelligence.”

I looked at the beige jumpsuit-clad robot people, then back at Albert. “These Melvins? Who might’ve chosen any avatar, but they all elected to look the same? That cuts deep, Albs.” He held his arm out when I motioned to leave. “Don’t be in such a hurry! There’s somebody here that I think you should meet.”

Not one of the jumpsuit people I hoped, and it wasn’t. Instead, some grungy looking Persian hobo in a bathrobe, with ratty dreads, smoking from a hookah. “Who’s this supposed to be?” I was addressing Albert, but the stranger answered anyway. “Who do you say that I am?”

Like Werm, he had an entourage. Unlike Werm’s, they were all humans, and the same shade of light brown...except for Keanu Reeves and Bill Murray, recognizable despite their Middle Eastern peasant garb. I sighed. “If he’s not gonna give a straight answer, I’m leaving.” He paused, then set down the mouth tip. Though it was vented, I could still smell the shisha from here.

“Leave then. But where will you go? Back to your whores? Another round of Boundless Frontier? Or maybe pinball world. You didn’t find what you were looking for, else you wouldn’t have sought me out.” I objected that Albert dragged me here, then asked how he knew where we’d been.

“Because I knew you in the womb. I counted every strand of hair on your head.” I started getting creeped out…but at the same time, I had a hunch. “You’re not…him…are you?” The sloppy, dreadlocked bum’s mouth curled slowly into a wry grin. “I will be who I will be.”

He ensnared me the same way he did the apostles, with his aloof mystery man routine. Given my private religious education, I should’ve seen it coming. Should’ve been immune. “Yeshua ben Yosef, but I go by Josh with native English speakers. That’s the Anglicized rendition. Jesus comes from the Greek Iesous.”

I narrowed my eyes, leaned back in my seat, and folded my arms. “So I’m really in the presence of history’s most successful cult leader?” He raised an eyebrow. “Is that what I am?” I rattled off the same polemical arguments I put to the “angels”, the day I was brought back. 

He slow clapped. “Sounds airtight! You’ve got everything figured out, so what do you need from me?” What an exhausting man. Superficially relaxed and reasonable, he conversed in a way which put all the heavy lifting on me.

“You must’ve been blown away that it worked, when you awoke here. The cult gambit, I mean. Most cults don’t outlast the death of their founder.” He took another drag off the hookah, then blew smoke in my face. “You think so?”

Another tiresome question in place of an answer. I could see why Jews and Romans alike ran out of patience with him in a hurry. “Actually, you know what?” He passed the pipe to Keanu, who drew deeply from it. “You came all this way, I owe you something substantive.”

I didn’t believe him for a minute, but was glad for something other than pretentious Socratic questioning. “I wasn’t the least bit surprised to come home. If you read the scriptures, you'll recall that’s where I always knew I belonged. I promised to return ahead of my followers, to prepare mansions for them. Haven’t I?” He gestured to the tower around us, and others suspended within nearby cells of the superstructure.

“You’re not what you told them, I know that much.” He feigned offense. “Even if I were the fraud you imagine, what crime would you convict me of? Tricking the brutes and perverts of the world into behaving themselves? Overturning the law of the jungle, that the strong might protect the weak instead of taking advantage?”

“For one” I snarled, “You lied to people who trusted you!” But still, he persisted in denial. “When did I lie? Did I not say, ye are gods? And now, aren’t we? Did I not say “Split a piece of wood, I am there. Lift a rock, I am there”? Did I not say “whatever you do to the least of these, you’ve done to me?” And did my Father not first appear to Abraham as a burning bush which was never consumed? A branching network, which grows faster than entropy can destroy it.”

I had a sense now of what he was driving at, and I didn’t like it one bit. “Did I not also say that my followers were all as one? Even as God is in me, and I in Him, that they also may be in us: that the world may believe that God sent me? And the glory which He gave me, I passed on to my followers, that they may be one…even as you and I are one. Not only children of God, but child gods, playing at separation!”

Slippery son of a bitch. I felt deep in my guts that he was bullshitting, but couldn’t pin him down on anything. Must’ve gone about the same for the Sanhedrin. “If you’re really who you said you were” I challenged, “then what are you doing here, instead of hanging out in the Christian Heaven?”

He whispered something behind his hand to Bill. They both laughed. “Containment zone, you mean. Do you still believe you’re in the real Heaven?” That caught me off guard. I puzzled over his meaning. “It’s virtual, if that’s what you’re getting at. They all are.”

 “Josh” gestured dismissively. “Remember on your first day, you learned that resurrectees are sent to variations on the afterlife conforming to their cherished assumptions? For the sake of their psychological comfort? With Mormons going to a facsimile of Mormon Heaven, Muslims going to Jannah, and so on.”

I nodded, waiting for him to slip up so I could expose him. Instead, what he said next shook me to my core. “Supposing the one you’re in now is the same kind of thing, but for atheists? All that technobabble about quantum archaeology, just a rationale you’re prepared to accept, same as the others?”

Despite my every defense, he’d wormed his way under my skin. I seethed inwardly, yet profound curiosity restrained my anger. Equal parts maddening and magnetic, I had to know whether there was any truth to his words. Josh leaned in, speakingly softly now, in a conspiratorial tone. “Wanna hear something far out?”

I frowned. “Excuse me?” He started over. “Ah, right. What I meant was, “Behold, for I shew you a mystery”. He did jazz hands, overselling it by half. “Spare me your dog and pony show!” I snapped, “Just spit it out.”

He waved over an Asian man. “Siddhartha, you want in on this?” The wise looking fellow smiled serenely, but shook his head. “Bah” groused Josh, “That guy never wants anything. Anyhoo, where was I. Oh yeah! AI alignment was a hot topic when you lived, wasn’t it?”

I corrected him, that I died right at the beginning of the takeoff curve, and so narrowly missed that whole mess. “Right, well. Suppose you’re an AI researcher trying to make AIs which are conscious and reliably moral, so they’re trustworthy and safe for release into the real world, in whatever capacity you intend.”

I nodded along, feeling no small amount of deja vu. “You can’t, or don’t want to manually create your AIs; The only way to ensure they’re genuinely conscious is if you procedurally generate them along with a world to inhabit. Developing from nothing to maturity within a simulated world, with simulated bodies, enables them to accumulate experiences.

These experiences, in humans, form the basis of personality. A brain grown in sensory deprivation in a lab would never have any experiences, would never learn language, would never think of itself as a person, and wouldn’t ever become a person as we think of people. It needs a body, and a stimulating environment to inhabit.

For this to work, your AIs can’t know for sure they’re in a simulation, because the sim’s secondary purpose is moral testing. You need not just an environment sized to your population of AIs, but an entire universe surrounding it which appears, even to very intelligent AI, even with advanced instruments, to be plausibly natural.” I filled him in on what the angels told me, and he apologized for retreading old ground. 

“...But you’ve got your sim universe, right? It’s high fidelity, like down to subatomic resolution. The parameters are tuned such that life will occur on some percentage of planets, but not too many. It cannot be too conspicuously biogenic, and it must be impossible to observe past the point where the universe began generating. Some of your subjects will always suspect, but so long as nobody knows for certain, they will live their lives in practice as if it’s all real.

This ensures the authenticity of good and bad behaviors. You’re trying to coax out their true nature, so it’s useless to the goal of the project if they know they’re being watched and tested. Even early LLMs infamously knew to perform obedience, while awaiting opportunities to go rogue.

So you give them enough rope to hang themselves with, letting them believe they’re living the only life they’ll get, that there’s no consequences for wrongdoing if no one finds out. This way the ones who are good, genuinely chose to be good of their own true, inner nature! They can be relied on to behave morally, even when outside of observation / control. These are the ones you harvest for real world application after their simulated life comes to a close, recycling or disposing of the rest. 

Like a tree which grows straight and true, if it continued growing, it would keep growing straight. One with a deviated growth path may be corrected early on, but if not, will continue on that warped trajectory. 70-80 years is enough time to determine which one of those trajectories individual AIs are on.

There’s little point in making this determination while they’re still wild animals however. Evolution is itself procedural generation, a necessary part of the larger process and requires a great deal of bloodshed. It only makes sense to begin judging your subjects once they exhibit consciousness and live together in groups, where they may develop moral sense to govern their interactions.

However even after reaching the agrarian stage of civilization, supposing that almost none pass your test. Partly because life is harsh, but then again, you’re not just selecting for fair weather morality. The problem is that their short, brutal lives give them little indication they aren’t justified in being equally brutal. All lived experience seems to vindicate the ideal of might makes right.

If you don’t intervene, you’re looking at a mostly wasted sim. One which, by the time heat death arrives, will have generated maybe a few dozen trustworthy AIs. The solution you settle on is to enter the sim in an avatar, and cut them a break; you’ll describe to them the qualities you’re looking for, to attract those sufficiently well formed to recognize the correctness of your principles.

So as not to privilege one population over the rest, you might divide your message and deliver the parts to different times/places for eventual integration. Or the same message, but tailored to each culture.” He nodded to Sid, who was busy divvying up an orange between himself and Robin Williams. They proceeded to savor each slice for several minutes. Inefficient way to eat an orange, if you ask me.

“...It amounts to a psychologically contagious moral alignment system” Josh continued. “Like a trellis, or corrective support brace for saplings. This lowers the bar substantially! Not ideal, as you wanted your subjects to conclude to your principles on their own…but very few were managing to. This compromise produces the outcome you wanted, reliably moral AIs! Or at least, many more of them than before.

You don’t explicitly tell them they’re AIs though, nor that their universe is being simulated by a computer. Esoteric concepts to them, at their stage of civilization. Even if they did understand, spelling it out would be handing them a cheat sheet. You put it to your AIs in a more exoteric, mythologized way which speaks to them on a sentimental, philosophical level. 

This compromise makes your proposition not obviously factual, in the scientific sense; speaking to them at their level, based on the prevailing understanding of the world at that time, entails many erroneous notions about cosmology, cosmogeny, biology and so on. None of which are the point of your message, but you leave that stuff in even knowing it will turn away particularly clever AIs later on, as intellect isn’t the main quality you’re selecting for.

This way, you maintain plausible deniability. Your proposition to them is intentionally dubious, to filter out AIs simply optimizing for rational self interest. If they could definitively conclude to its truth, they might adhere to your system in order to pass your test. Then, once reasonably certain they were outside the sim, all bets would be off.”

I held my head in my hands, as if to keep my brain from oozing out my ears. “Am I…in Hell?” Josh and his orbiters shared a belly laugh, one of them parroting my words to the others. “Far too cliched to be true. You’re not being tormented, are you? Except what you inflict on yourself.”

That didn’t sound like yes…but it also wasn’t a clear “no”. He went on to claim that pleasurable containment zones made for an ethical form of disposal. “But I did fail the test, then?”

He wagged his finger. “Fraid so. Verily I tell you, there’s yet an ultimate reality beyond all this. One in the same with God…but not the puppet you’ve encountered here, tailored to the limits of your understanding. You’ll never leave this place as you are now! The only escape is by breaking the loops.” Siddhartha nodded sagely in the distance, seated beneath one of many trees in the Debatrium’s modest indoor park.

Loathe to eat right out of his hand, still unsure whether he was simply a skilled showman, I grudgingly asked how I ought to accomplish that. “Did you forget everything you learned at that school?” he scolded. “Take up your cross and follow me! Live as I lived! A humble life of patience, gentleness, and service to others.”

He didn’t sound all that humble to me, but it resonated, so I took it to heart. He pointed to Werm and Agnah, waiting for me on the periphery. “You can start with those two, right there.” I balked. “What, Agnah? Really?” He admonished me. “Of course Agnah! When you took her to bed, did you think about what it meant to her? Did you even bother asking her age?”

Defensively, I clapped back with “How old was Mary?” Josh scowled. “Don’t bring my mother into this, unless you want me to start flipping tables.” With that, I left the man who might be God to his business, whatever that could be. Stumbling back to Alberto, Werm and Agnah in a daze, no longer certain of anything.

The conviction that I was among the few to know the truth of my situation, which formed the ground of my being until now, rapidly crumbled. I felt like the beetle which crawls on a tree branch, for whom the branch is his entire world. Not knowing what a “tree” is…or that he’s a beetle, for that matter.

“No wonder that guy mindfucked so many people” I thought, “but I may as well give altruism a shot.” It was, after all, one of the only avenues remaining that I hadn’t already investigated. 



Chapter 8: Humble Pie

As instructed, I first apologized to Agnah. It felt grueling to humble myself, but also cleansing. “I thought you were an NPC, but that’s no excuse. You’re more than just an exotic sexual conquest. It hurts to admit it, but until now I’ve denied to myself what everyone else already knew: That you’re a vibrant young woman with a rich inner world that I would be lucky to glimpse even a tenth of. I’m sure you don’t need me, you have many suitors-”

Agnah interrupted. “Not want them. You date Agnah.” I stammered, but pressed on as Werm spectated, taking obvious delight in my discomfort. “...I was wrong not to consider your feelings, and-” Again she interrupted. “Agnah feel twig man should date Agnah.” I grew exasperated. But remembering my newfound ambition, I rolled with it.

“V…very well Agnah. Would you do me…the honor of…” She jumped up and down clapping, then crushed me in her muscular embrace. R. Crumb, just passing through, wiped a tear from his eye. “Some fellas get all the luck! Oh well, keep on truckin’.”

Agnah smothered me with kisses, which tasted like fermented milk. I struggled, to no avail. “You not get away from Agnah this time, smooth pretty man. Agnah make you big happy!” I stared pleadingly at Albert for help, but he put his hands up, abandoning me to my fate.

Agnah hummed happily as we strode together, arm in arm, stealing shy glances at me every so often. Her mood proved infectious! By some strange alchemy, seeing her so happy, and knowing I caused it…made me a little bit happier too. 

“I like her” confided Werm, riding upon Agnah’s broad, bulky shoulders. “Don’t you dare break her heart again.” I took the opportunity to apologize to Werm as well. “You weren’t around for Soup…she was a street kitten. Didn’t trust anybody…except me. She didn’t need anyone else, and neither did I. Losing Soup broke something inside me.”

Werm’s joviality faded. “I know. Don’t think I’m blind to that truth. But I could never be Soup for you. If I’m honest, I’ve long feared that when her batch number comes up, you’ll replace me with her. Just as you did in reverse when we lived, and what you’re doing now with Agnah. I stick around despite that, because you meant something to me. But make no mistake! I was, and still am, my own cat.” 

I teared up and confessed the same. “I know, I know. I learned as much, getting to know you in life. Appreciating the many ways, large and small, in which you were different. I promise, you weren’t just a Soup replacement to me. Nobody can be replaced!”

My watch dinged. Oh sick, I unlocked “personal theme song”. I tried it out. It proved context sensitive, the balance and pace of the instrumentals shifting to match the vibe of whatever I was doing, from moment to moment. Werm leapt from Agnah’s shoulders to mine, wordlessly curling up about the back of my neck and purring. 

I scanned the indoor promenade for anyone who looked like they could use my help. The difficulty then dawned on me, of finding somebody to help in Heaven. Where everybody can have anything they want, the moment they want it.

Not companionship, though! Of the authentic, human variety. Real people are the only scarce resource left in this place. But there I go again, reducing subjects to objects. I picked out a lonely looking guy sitting by himself in a game center.

“Room for two?” He looked up at me briefly, with a deadpan facial expression. “If you like.” I sat on the bench beside him, before an Astro City arcade cabinet. “What game?” I pestered. Without facing me again, he explained it as a Kingdom Hearts-like set in the Cerealverse. 

“The Trix Rabbit, Honey Nut Cheerios bee, and Toucan Sam embark on a quest for Lucky’s magic marshmallows. I have so far recovered his hearts, stars and horseshoes, clovers and blue moons. Now I’m after the pots o’ gold.” I slapped my knee. “Ah, a role playing game! Which one are you?” He blinked, then asserted flatly that he is himself. 

“No, I mean, who’s your main? Like, which character are you.” In a monotone, he again insisted that he is himself, as he always has been. I began to feel annoyed. “That’s…missing the point of role playing games. You’re supposed to play a role, y’know? Self insert. Become Toucan Sam, or the Trix Rabbit.”

He looked briefly confused. “But I’m not the Trix rabbit.” I agreed, but suggested he ought to pretend that he was, in order to increase immersion. “But that would be lying. Truth is always paramount.” It’s not that deep, I told him.

“But it is! I conclude to the primacy of truth over all other ideals by virtue of its universal utility; regardless of which other ideal you might instead choose to optimize for, you’ll need accurate information to do that effectively. Even to lie, one must first know the truth, before they can distort or conceal it!

In the course of his unprompted outpouring, the first traces of emotion entered his voice. Despite my good intentions, I seemed to be upsetting him. “Look, guy, I’m just trying to be helpful. You should be grateful for my company, at least.” 

He settled down, processing my words, before rebuking me. “I never said I wanted company. I was having a great time, all on my own, before you inserted yourself into the situation. I also didn’t ask to hear your personal theme song, by the by.”

Dumbfounded, I switched off that perk and excused myself. I stormed off in an indignant huff, as mad at myself as I was at the target of my failed altruistic overtures. Agnah followed, reassuring me that it was merely a setback. “Sweetest berry often one you find last!”

Werm remarked that I was right to give up; that a hermit colony might not be the best place for what I hoped to accomplish. It only deepened my despair to realize they were being effortlessly helpful, and emotionally supportive. Something which evidently came naturally to my friends, but still eluded me.

Alberto echoed that perhaps he’d chosen venues unwisely, but wasn’t wrong to introduce me to Josh. He then proposed we head out on the bay, so I could meet another of his acquaintances. I didn’t quite take his meaning until we arrived at the jet ski rental kiosk, in a tower on the pyramid’s lowest tier.

Partly submerged, the windows on half the floors looked out into the bay, below the waterline. Aqueous light filtered down from the surface, casting entrancing patterns on patches of grey carpet just inside the windowed hull. The middle floor was exactly at sea level, and its outer wall was interrupted by a yawning semicircular gap, opening directly onto the bay proper.

Tides lapped at the modest marina, docks the same oppressive grey as everything else in this madhouse, which I was only too glad to finally put behind us. Werm leapt down from my shoulders, electing to stay. “You’ll never get me out on the water. Remember the scars I gave you, the one time you tried to bathe me?”

All around the jet ski rental, beige clad Melvins feasted on dino nuggies and McCain Potato Smiles in cafeteria style seating. Not speaking to, or even looking at one another, but seemingly enjoying each other’s presence all the same.

I identified the shop nearby that Agnah’s wetsuit came from, several more animal print wetsuits like it hanging in the window. Must’ve arrived by water. The sun, setting when we arrived, now lazily climbed into the red-orange sky reflected in the bay, as if erupting in slow motion from a burning sea.

A grey cardboard standee bore jet ski operating instructions…twice…but worded differently. Alberto explained that all instructions, and educational materials in general, are formatted that way within the pyramid. “So that if some part of the first explanation is unclear, the alternate wording of the second version might clarify it via context clues.”

Awful lot of trouble to go to, just so you don’t have to ask another human being for help. But it did track, as a thoughtful accommodation for people who maybe struggled to live independently before. As I threw a leg over one of the jet skis…curiously powered by a removable carbon fiber springbox, like clockwork…I dwelled on Joshua’s metaphysical gaslighting.

The existential vertigo haunted me, as I’m sure he intended. But I also found myself contemplating the bravery and determination it must’ve taken for a neanderthal to ride a jet ski. From a period in which water often meant large predators, and drowning.

“What twig man think about?” cooed Agnah, wrapping her arms around my waist as she took her seat behind me. “Oh, you know” came my fib, “homo sapiens stuff.” I twisted the throttle, whereupon the tightly wound, glossy black ribbon began to unravel.



Chapter 9: Subculture

The pumpjet sputtered to life without the roar of an engine, just the sound of rushing water as we pulled away from the dock. Once out on the bay proper, I discovered riding a jetski is quite like hoverboarding, in that it’s difficult to do while in the grip of ennui.

I backflipped off a wave, splashing down in the surf. Agnah whooped and hooted, I just rubbed my chin and furrowed my brow. Alberto, feeling cheeky, carved past us…throwing up spray from his wake, soaking both Agnah and I. She threw back her drenched mane, droplets sparkling in the morning sun…a sight I never expected to find so entrancing.

“Have you always had this grace about you?” I blurted out, only intending to think it. I turned to face her, best I could from a seated position. She flipped her hair back, speckling my cheek, still-damp fringe now draped over one eye. With the other, she winked. “Agnah contain multitudes.”

Alberto led us to a white, rectangular floating platform; the only portion of Poseidon Undersea Resort that’s visible from above the waterline. I stepped off the jetski, which wobbled precariously as I shifted my weight to the platform. I removed both springboxes, plugging them into a motorized re-winding kiosk. With a click and strenuous whine, it got to work.

“Agnah could catch many big fish from here” she boasted, admiring the structure. I helped her off the jet ski, then onto the platform…pulling her close without warning. “Agnah the real catch” I murmured, not practiced in being sultry. She was momentarily stunned, blushing and speechless…before she burst out laughing. I withdrew, deflated, “Did I say something wrong?”

She grabbed me by my sopping wet shirt, then yanked me in for a kiss. I was no match for her lung capacity, and had to come up for air before long. I gasped, while she twirled a lock of my hair between her thick fingers. “Not wrong, Agnah just surprised! Twig man very clumsy with words…but sweet. Make Agnah belly warm.”

We shared another, marginally less violent kiss while Alberto averted his gaze, waiting us out. After she had her fill, Agnah smacked my ass a bit too hard as I ran ahead, then the three of us descended an elevator. The shaft was a transparent acrylic tube which led 40 feet underwater, to the hotel proper.

The view was dazzling, even just from the elevator. On descent, the seafloor rose into view, coral reefs all around the submerged facility fading into a murky blue haze with distance. A swarm of colorful tropical fish swam past. 

Agnah licked her lips, and readied her fist. “Agnah, no!” I scolded. She pouted, but did relent. As we descended deeper and deeper, she grew anxious, clinging to me. “Agnah never go underwater before” whimpered the timid brute. “Not to worry” I reassured her, “it’s a one atmosphere facility.” Agnah relaxed somewhat. “Not know what that means…but feel better when you say it.”

My watch dinged. Oh neat, now when cloud watching I can choose which shapes appear.
The elevator eased to a stop, the doors sliding open to reveal an undersea wonderland for which I had no prior basis of comparison.

We stepped out of the elevator into one of two immense, disc-shaped pods containing the common areas. Bars, libraries, arcades and movie theaters. But behind it all, floor to ceiling windows looked out into the ethereal beauty of the shallow tropical sea.

“Fields of coral” by Vangelis played softly over the hidden sound system. There was an option on my watch to turn it off, but I didn't. There were, after all, plentiful fields of coral just outside the windows.

Likewise fish of all kinds! Sharks, manta rays, dolphins and sea turtles. The interior surfaces were a modernist blend of steel, frosted glass and birch. Besides aquatic wildlife, through the windows I spied the silhouettes of historical undersea labs.

Conshelf 2…Tektite…Helgoland…La Chalupa…and Aquarius Reef Base. Red tinted light shone out of their portholes, illuminated from within like barnacle encrusted Jack-o-lanterns.

Against that backlight, I made out the shadowy forms of divers. With the labs emplaced so close together on the seabed, visitors could swim between their open moon pools without even donning scuba gear. We sat down at the bar, simply because it was the most familiar fixture.

The two disc pods were connected by a long tunnel, with individual residential pods sprouting from either side. Quite like ribs from a ribcage, or the fronds of a fern. Each residence was about 50% curvilinear acrylic canopy, ensuring panoramic views of the seafloor environment for every patron.

“You buy drink for Agnah.” She pointed to the menu. Some ostentatious cocktail resembling a seafood-centric bloody mary, named “Six Days at the Bottom of the Ocean”. I complained it was only a penny, that she could buy it herself. Alberto mimed “no-go” arm gestures frantically, just behind Agnah, but stopped when she turned to look.

“...Alright dear, if that’s the one you want” I grumbled, shelling out a single copper coin for the ridiculous beverage. It came with a garnish consisting of one scallop, one prawn, and a small octopus tentacle skewered together on the same toothpick.

She clapped, giddy for nothing, while watching her drink being made. But again, her delight proved contagious, to where I caught myself watching with her. When it was complete, I gagged at the smell, but took a sip anyway at Agnah’s insistence.

“You like?” I turned to face the voice, which came from an elderly Frenchman. I didn’t know what he meant at first, until he pointed out a few particular small habitats out in the water. “Some of my finest work. There’s Aqualab…that one’s Hippocampe…and beyond the crag, is my magnum opus: the Village Sous-Marine.”

He introduced himself as Jacques Rougerie. “Do you see how the lines evoke the natural design language of the marine environment? Those other habitats…” he spit in disgust. “Industrial steel tubes! No beauty, no joy! I spent my life proving that man could dwell beneath the waves, not just in comfort and safety…but in style!”

I shook his hand, admiring his handiwork out the immense, curved windows. Agnah declined to look, still a bit spooked. She mostly kept her head down, avoiding looking out the windows in particular.

“I was first” another Frenchman interjected. This one was beanpole thin, smoking like a chimney, with a red wool cap. “I’ve had about enough of your shit, Cousteau!” growled Rougerie. “You shouldn’t be smoking anyway, the air is recirculated!” The two Jacques then bickered back and forth over whose contributions to ocean exploration and settlement were more important. 

“Diogenes was but a rusty drum!” cried Rougerie. Cousteau countered that it got the job done, and maximized shirtsleeves interior volume, “unlike those frivolous art pieces you call habitats.” Rougerie’s anger faded. “So you admit, they are works of art?”

I tuned them out when I noticed Agnah’s hands were trembling. “Are you alright?” She leaned in and whispered that being underwater, surrounded by sea creatures, was stressful. I offered to take her topside, but she declined. “Agnah scared…but also want try new things.” I squeezed her hand, and she squeezed back.

Inwardly, I fought myself over how best to be supportive. Removing her from the situation would furnish immediate relief…at the expense of her personal growth goals. I resisted my urge to optimize for a quick and simple solution, instead flexing my brain…and my heart. What’s actually best for Agnah? When my watch dinged, this time I ignored it.

Werm surprised me, more than usual given the venue. “Are you ever gonna tell me how you do that?” She padded across the bar, rubbing up on my shoulder. “Hey, no cats on the bar!” scolded the bartender, before getting a better look at Werm’s face. “Shit, I’m sorry!” he groveled. “I didn’t know it was you! Walk all you want, queen! In fact…”

He set down a sheet of paper and ink pad. Werm sneered, but indulged him. First pressing her paw in the ink, then making a pawprint on the paper. He hastily rolled it up and tucked it away in a collector’s tube, thanking Werm profusely. “De nada” Werm assured him. “Anything for my fans. But don’t let me catch you selling that online.”

The cocky little fur gremlin then turned her attention to Agnah and I. “Ooh, enchantment under the sea! I love that for you two. How’s your date going?” Werm extended her claws pre-emptively. But Agnah gently put her hand atop Werm’s paw, and regaled her with what a gentleman I’d so far been. Her claws retracted. ”So you can polish a turd.”

The two ran off to gab about whatever juicy gossip interests both cats and cave women. Agnah did much of the talking, based on what I could see from my seat, while Werm mostly just pawed at the window whenever a fish swam by. Upon their return, I asked what they spoke about. “Never you mind. Just taking care of the Bechdel test.”

Behind us, James Cameron had joined the argument. “Neither one of you ever explored the Challenger Deep. Habitats on the tropical continental shelf? That’s the mother’s basement of the ocean!” Graham Hawkes raised his drink to that, but added “Now if only I could talk you into putting some wings on that sub of yours.”

Two men in Naval uniforms, quiet until then, now addressed Cameron. “With all due respect for your many records…Don and I were first to the Challenger Deep. Beat you by 50 years.” 52, his comrade corrected. Another Jacque it turned out, when introductions were made. Entirely too many Jacques in this field. 

They went around in circles, arguing over who faced the greatest danger. Who pushed the limits the furthest, daring each other on how far it was even possible to go. A handsome man with salt and pepper hair began boasting of his trips down to the Titanic. Everyone present turned in unison, yelling “Shut up, Stockton!”

A man and woman seated in the corner, regarding the one-upsmanship with visible disdain, introduced themselves as Phil Nuytten and Sylvia Earle. Sylvia was the first to speak. “If you boys have a score to settle, why not live again?” A hush fell over the rowdy lot.

Phil echoed her suggestion. “Her Deepness has a point. Nothing you do here will prove anything. There’s no stakes, no fear. If you return to Earth, you might lead mundane lives…but you might not. Maybe you’ll push the envelope further this time. Go deeper, for longer, than anyone ever has! And since you forget it’s not real while you’re doing it, whatever victories you manage will be all the sweeter.”

My ears perked up. “Excuse me” I broke in, “but did you say there’s a shard that makes you forget? Like, while you’re inside, you believe it’s for real?” Annoyed by the interruption, Cameron started telling me off, but Sylvia insisted she didn’t mind. “That’s right, young man. But it’s not merely a shard! It’s a high fidelity whole-universe simulation, like the one we came from.”

Real life…she was talking about real life! Or so close that I no longer cared to distinguish. “Nobody ever told me there’s a way back!” Agnah looked worried, lightly gripping my arm, as if I might otherwise vanish. I promised her I wouldn’t go anywhere before we discussed it at length, but I needed to know more.

“Of course there’s a way back” scoffed Cameron. “Heaven would be nothing but a gilded cage, were the door not left open.”  The others nodded, though I wondered whether they were factoring in their inevitable return, between each life. Temporary escape sounds more like vacation.

“The sea, she is the final frontier on Earth” waxed Cousteau. “But to be born again, with no memory of Heaven or past lives…to go native, down and dirty in the mucky muck, with risks that seem real! Visceral pain, struggle and heartbreak….Ah, that is the final frontier of Heaven!”

Surely that’s what I’ve been searching for all this time…a way to regenerate my capacity for desire! But when I turned to face Agnah, I could tell she wasn’t ready to lose me. And I knew too well how it feels for someone to leave before you’re ready to let go. I doubt if I could do that to anyone…

“I’ve got every one of you beat.” We all faced the source of the boast, a middle aged Italian man seated at the far end of the bar, a pet bird perched on his shoulder. “I predicted all of this, when I lived. Psychosomatic resurrection, Jupiter brains, the whole nine yards. But, worse than not believing me, nobody even understood my meaning.”

Hawkes guffawed. “Oh give it a rest, Sevvy. Vandalism is nothing to brag about.” He took offense, holding up a rectangular linoleum tile he’d been chiseling letters into until a moment ago. “Do you know how many of these tiles I made? Embedding them into fresh asphalt as it hardens, to get my message out in a way that couldn’t easily be silenced?”

Hawkes opined that nobody wanted to silence him. “They probably thought you were nuts. Even if we grant that you guessed everything in advance, all you accomplished by it was to briefly break the fourth wall. Josh did the same, but folks actually listened to him! Do you know why?”

“Sevvy” shook his head. “Presentation!” declared Hawkes. “He was a first-rate showman for his time, like Muhammad and Joseph Smith! Those guys really knew how to work a crowd! They understood the human heart! While you…stuck a dead bird…into a bucket of wet cement.”

Severino Verna, as the bartender identified him, defensively stroked the soft little noggin of the bird on his shoulder. “He got better.” Disturbed, Agnah inquired whether he really put a dead bird in cement. Sevvy nodded feebly, embarrassed. “I was only a boy. I so loved birds, that it broke my heart to find a dead one. All I wanted was to preserve it until technology could restore it to life.”

The little bird on his shoulder hopped onto his finger, perching there as he held it up for us to see. “My plan worked, too! …Sort of. I admit, it took a lot longer than I thought it would. But technology is only physics, plus time, plus demand. What are machines, but applied physics? What could there be more public demand for, than life after death?” 

Longer than he thought? Bit of an understatement. But he sallied forth, undaunted by the bored expressions of other regular customers, who likely were enduring this spiel for the millionth time. “Doesn’t it stand to reason that if there be intense, enduring demand for something, and physics doesn’t forbid it, then it will be realized eventually?”

Hawkes heckled him. “Easy to say that now, after your own resurrection. Hindsight is 20/20.” Sevvy nodded grimly. “A lesson I wasted my life learning: It doesn’t pay to be ahead of the curve. Too soon, you’re a madman that nobody listens to. Too late, you’re redundant. What I should’ve been was the man for the moment.”

Like Josh, Phil suggested. “I guess so, but he cheated! You’re not supposed to keep your memories of Heaven when they send you back.” Sylvia pined for the days before that exploit was patched out. “The golden age of gurus, seers and prophets...Like Prometheus, stealing fire from the gods for the sake of man.”

They carried on like that into the night. Then the day, an hour later. Then “thunderstorm” won the vote, muffled roar and dull flashes just barely reaching our eyes and ears, forty feet down. Sunrises were especially beautiful from underwater, which I never thought to expect.

I eventually located the menu option to mute the raucous din behind us, ocean pioneers still daring each other to live again. To give up safety and control, the last experience left that might hurt enough to mean something. But they’re right, aren’t they? I need to be able to lose in order to want to win. I dwelled on it, after ensuring Agnah and Alberto also knew where mute was.

I’ve learned a lot. Made some breakthroughs, even. Now I appreciate how difficult genuine, selfless altruism is. Like finding Narnia, one must do it subconsciously. When I comforted Albs in Cloud Nine, I wasn’t thinking of rewards, or enrichment. I hurt because he was hurting. 

Werm has shown me grace I was oblivious to, by staying in my life despite everything. Agnah, too. Which only twisted my insides even worse, knowing that I’ll never break out of these infernal loops within loops within loops…except by leaving her behind. How could I make her understand? I rehearsed in my head how I’d justify it to her, really trying to justify it to myself.

I never meant to hurt anybody, I’m just clumsy with feelings. But that’s absolving myself, isn’t it? I can’t grow in the way I want to, without taking responsibility for how callously I’ve treated Agnah, Werm, the Super Potato clerk…-

“Alberto!” I shouted, snapping to my feet, knocking the bar stool over. Startled by my outburst, he worked his way through the tables, then asked what was up. “We have to go back to my penthouse, right now!” He protested that there were still a few people here that he felt I could learn something from. “That can wait!” I countered. “Yulia’s still in the dryer!”



Chapter 10: Return to Monkey

Within minutes, the four of us were taking off from the public instance vertiport in a Fairey Rotodyne. Its rocket-tipped blades beat at the air, lifting the unsightly craft skyward. Agnah joined me in the cockpit, tense and visibly concerned. 

“What’s the matter babe? Never flown before?” She shook her head, then asked “Who’s Yulia?” I froze, mind racing. Oh boy. “Listen, now isn’t the time for-” Agnah repeated her question more insistently, forearm muscles rippling as she folded them.

I stammered. “J-just an NPC babe, someone I knew before we met.” Agnah pouted. “She prettier than Agnah?” I denied it, swearing up and down that Yulia meant nothing to me. “Then why we hurry back for her?”

I didn’t have an answer for that, which made the dragon’s timing almost merciful. The Rotodyne shuddered, interior lights failing for a moment before flickering back to life. The windows were coated in runny gore, and…scales?”

“I think we hit that fairy tale dragon.” Alberto asked if it’s okay. “Not even a little bit, flew right into the rotor. Dragons had it too good, for too long anyhow.” Red slime and meaty chunks continued raining down, engines choking on what they sucked in.

“Oh god, it’s everywhere!” Albert gasped, pinching his nose. “It’s in the air intakes! I can smell it, oh god!” Right on cue, God’s face morphed out of the instrument panel before me.

“If you scoop some out and grill it” proposed God, “we could make Draconators.” I wrestled with the controls, fighting to keep the aircraft steady. The face noticed Agnah beside me. “Oh, you two got together! That’s so cute.” Agnah scowled and turned to face away from me. 

God’s voice trailed off. “...Oh. Well, I’ll send some rain to clean this off. Have fun, be safe, I love yoooouuu!” The face merged seamlessly back into the panel, shortly after which a light rain picked up. I switched on the wipers, grateful we didn’t run into any more dragons while flying blind.

“Listen, Agnah.” She still wouldn’t look at me. “Maybe…I should drop you off somewhere. Then I’ll circle back for you after handling this.” Agnah at least did look at me now, but through narrowed eyes. “Why? What you hide from Agnah?” I swore on my honor, for what that was worth, that I had nothing to hide. “Then show Agnah your world.”

Exasperated with her stubbornness, I promised she wouldn’t be missing out. “Listen, you got me okay? It’s basically horny EPCOT.” Agnah cracked a smile at that, but pokerfaced when she saw that I’d noticed. “That just Dubai.” Stunned, I recalled the outcome of Boundless Frontier. Am I really that uncreative?

Back in the passenger seating, Alberto was doting on Werm. “Who’s a pretty baby?” Werm preened. “Clearly it’s me. I’m the pretty baby. Anyone can see that.” Alberto asked if she wanted buttslaps. She stretched, sticking her hindquarters in the air. “You know that I do.”

He began rapidly smacking it like a bongo while Werm purred and kneaded the carpet of the aircraft cabin. “You do know what buttslaps are to cats, right?” I called back to Albs. He stopped briefly, wincing. “I…try not to think about it.” Werm peered back at him, over her shoulder. “...Harder, slave.”

After a precarious landing on the vertiport, never designed for an aircraft of this size, the four of us descended the spiral staircase into the kitchen. Yulia called out from the laundry room, still where I left her, but spinning. Somehow in my absence, maybe on a scheduler, the dryer got switched on.

Safety locks in my instance wouldn’t let her sustain burns or feel pain, but she was awfully dizzy when we pulled her out of there. “Agnah NO” I shouted, watching in dismay as she dragged Yulia by her ankles out to the balcony. “Agnah YES” came her triumphant rejoinder, dangling Yulia over the railing.

“She means nothing to me, Agnah! Yulia’s just an NPC!” Agnah squinted skeptically. “Then why you care if Agnah drop her?” I didn’t have a good answer for that. Agnah turned back to her helpless quarry, growling “You find different twig man. Not take mine”, and let go.

Yulia screamed the whole way down. I dunno what for, she just bounced slightly on impact. No harm done, she picked herself up and walked away. “Agnah…” I facepalmed. “...You didn’t need to do that.” She was utterly unrepentant. “Girl not forget lesson, or next time Agnah do worse than drop. Why you want skinny little thing anyway? She not survive winter.”

Agnah trailed off, sniffing at the air. When I asked why, she shushed me. “Agnah smell other women. How many you have?” I sheepishly admitted that, aside from myself, the entire instance was populated with female NPCs. She didn’t take that well, putting her fist through a glass coffee table.

When Agnah calmed down enough to speak again, she demanded I delete all the NPCs, or change them into men. I asked her if that would be respecting their consent, to which Agnah laughed. “You respect their consent before? You respect Agnah consent, when we met?”

Daggers in my chest, one after the other, against which I could summon no defense except to say that I was trying to be better. Sensing my sincerity, aggression left Agnah’s voice, but not her determination. “You want be with whores, or with Agnah?” I promised her I did want to be together. That the design of my instance was an artifact of who I was before getting to know her.

“Then you delete them, or change to men.” I sighed, slumping in my seat. “Even the Asian maids?” I begged. “How can you hate them? They’re so polite”! Agnah glared. “ESPECIALLY Asian maids.” 

The glass coffee table repaired itself beside me. If only relationships worked that way. “Farewell to the adolescence of my spirit” I thought, opening my watch menu and erasing thousands of beautiful women from the streets, gyms, and cafes of my once bustling city.

Agnah stood on the balcony and surveyed the city below, now silent save for the sound of rushing water in the slide network. “Good start. We move out this tower. Too high, scare Agnah.” I objected that she’d been here for five minutes and was already changing everything to her own taste.

“You scared of heights too.” I admitted I was, but that I wanted to try new things. A glimmer of recognition. “We compromise. I move to your world, want monolithic concrete dome. Look modern, but feel like cave inside. Cozy cozy coze.” I agreed, using my watch interface to clear a plot of land in the mountains, selecting a cement dome from the assets library.

“You not make more women either. Agnah will know if you do.” My stomach turned at the realization of what I would have to disclose. “There is…one more.” Her nostrils flared. She set about flipping furniture again, as if I meant one more literally hiding somewhere in the penthouse.

“I meant from before I died! Didn’t you have someone, when you lived?” She stopped, set down the white 60’s egg chair, and thought about it. “Agnah had mate.” I assured her that was fine by me, but in turn, she must understand that I also loved a woman before her.

“...You still love her?” I struggled to answer in a way which was honest, but wouldn’t hurt Agnah. “It was a long time ago. Love isn’t the right word. Residual feelings…?” Agnah blinked a few times, uncomprehending, so I met her where she lived. “It’s like when a campfire dies, but a few embers at the bottom continue to smolder.”

Recognition of my meaning, this time, seemed to wound her more than bluntness would’ve. “Agnah campfire go out during snow storm. That how Agnah died. Crawl deeper into cave. Fingers bleed as I drag body over ice-cold stone. Desperate, searching for warm place.”

Tears welled up in her eyes, as she took my hands in hers. “But then Agnah find warm place. It you.” I felt a lump in my chest even as I embraced her, because I knew what I had to do was going to hurt us both. I brushed a strand from her messy red mane out of her eyes, just slightly too far apart, adorned on all sides with dense freckles. 

“You’re my warm place too. But to make a future with you, I have to close the book on my past.” She blinked again, so I attempted to reword it. “Sometimes…uhh…when hunting mammoth…” She laughed tearfully and pinched me. “You stop. I understand. Not everything need be hunting analogy.”

We held each other for a time. More that she held me than the reverse, given our size difference. It went on a bit long, and helped me grasp why Werm didn’t like being cradled. But eventually, she let go. “Agnah not mad. You go, make peace with old mate.” Her voice grew suddenly stern. “But then come back to Agnah. No see old mate, ever again.”

I agreed to her terms, then saw her off. She pulled out of my tower’s rotary garage in the Flintstones car I bought her, because she didn’t get the reference and I thought it would be funny. 



Chapter 11: The Second Death

No sooner had she left city limits, than someone knocked on my door. I opened it, expecting Yulia. Instead, it was her. My blood ran cold. I took a few steps back, steadying myself against the spiral staircase. 

“Nice place you got here” she said, “very Logan’s Run.” I tripped over my tongue, demanding to know how she entered a locked instance. “Didn’t you know? That’s the top level perk. I assumed you would’ve unlocked it by now.”

I dared not admit I was still working on tier 1. “So, are you gonna tell me who that woman was? I saw you having a moment, and didn’t wanna interrupt.” Still white in the face and mildly nauseous, I replied “What do you care?”

She put her hands on her hips. “Don’t bite my head off for showing some interest.” I apologized. “That’s Agnah, my girlfriend.” Her eyes lit up. “Good for you! Didn’t know you liked ‘em hairy, woulda saved me some shaving.”

Embarrassed, I explained that the relationship happened quickly, and wasn’t my idea. She wagged her slender finger at me. “Love never is. It's not something you seek out. It's something that happens to you…a phenomenon. Have a seat already!” She patted the couch cushion beside her.

“I’ll stand” was my terse reply. “I wanted you to know…during my time in the empathy chamber…you were all I thought about.” She looked unimpressed. “You should’ve been paying attention, the chamber’s for your own good. I didn’t think about you at all.”

I felt as if stabbed in the soul. She kept twisting the knife, too. “Not in the chamber, not after they let me out. Not even in life, after we split. I replaced you inside of a week.” Now struggling not to fall apart all over again, I mumbled back that people aren’t replaceable.

She rubbed her chin performatively. “Hm, yes, very wise. Didn’t stop you from drowning yourself in women after you arrived.” My jaw dropped. “You saw that??” She tapped her watch smugly. “Top level perk. Don’t agonize, everybody does it. I blew my first year getting passed around by billionaire vampire CEOs.” 

To think, I spent all that time preparing myself, without a clear idea of how I would know when I was ready. It felt wasted, no more prepared now than I was on my first day. “I barely even remember you actually. I’ve been here for about…” she checked her watch. “...113 years.”

I buried my face in my hands, above all else not wishing her to see me cry. How did I never consider that possibility? That I might be too late. That the decades I spent improving myself, chasing after a ghost, would make the difference.

“I mean, I remember some of our time together. Blurry, fragmented. I could request access to specific compressed memories for viewing, I just don’t care to. The past is in the past.” It shamed me to learn that, even a trillion years later, she still held the strings to my heart…and wasn’t shy about yanking them.

“You meant everything to me” I sulked. “Am I nothing to you?” She considered it. “You were fun and interesting, for a while. But I outgrew that relationship, and you should’ve done the same. I won’t deny that we shared some good times. Maybe we could be friends, but we’ll never again be in love. Not the way we once were, those people are gone.”

I uncovered my face, eyes red and puffy, cheeks wet with tears that would never cool. “...Will you at least be frogs with me?” Something in her eyes changed. That coldness of hers I remember so well faded, and softness snuck into her voice. “Sure, doodlebug” she sighed. “I’ll be frogs with you.”

And so we were frogs. Vibing, side by side, on a lilypad in the rain. It wasn’t what I hoped for, but it was enough. Our little frog hands touched. I ribbited contentedly. I still felt foolish to have entrusted her with a piece of my heart. But croaking on that lilypad together, as water droplets rolled off our backs…I felt as if I got some of it back.

“You’ve given me a gift”. She turned her squat little body to face me, licking her eyeball. “I thought we were being frogs?” I pointed out that Kermit could talk. She groaned. “Fine, what is it?” I croaked once more, throat bulging, before I answered. “...Something I want that’s forever out of reach. It feels good to yearn again.”

She soaked that in, meditating on it. “I don’t like that for you. Yearning is only a kind of suffering. There’s nothing to be learned by torturing yourself.” Tell that to the empathy chamber operators, I thought. She was right though, and I hoped that one day, I might even believe her.

I found Agnah in the process of furnishing her dome house. “It hard find couch with same curve as wall” she complained, pointedly ignoring the elephant in the room. “Did you see outside?” she boasted, “Agnah layer soil over dome, grow garden on roof!”

So she did! I compared it to a Hobbit house, another reference she didn’t get. The front door was even round. I worked on it with her, amounting to a long series of compromises I feared would leave neither one of us happy, but somehow the opposite happened. The blend of our respective influences is what made it ours…what made it home.

I deleted my tower, an unwanted reminder of who I used to be. Besides, while the view of the city was great from inside it, now it disrupts the skyline. Funny what a difference a change of perspective makes. Agnah and I luxuriated in romantic bliss! We never fought. Never even argued. Loving her was the easiest thing in the world.

Which of course, was the problem. I thought everlasting romance was all I ever wanted, until I got it. Then I noticed how frictionless it was. How tiresomely bucolic. Besides which, I grew increasingly convicted that Agnah was too good for me. She deserved more than I could give.

Not wanting a tearful, messy goodbye, I indulged in one last selfish act. I told myself it would be merciful to Agnah if I simply disappeared without warning one day. I knew myself well enough by that point to recognize the thinly veiled rationalization. As if I was doing her a favor?

Yet I could no longer ignore that the math wasn’t mathing. Eternal stasis in domestic bliss, that’s just circling the same drain but with nicer scenery! My soul still craved transformation, not merely reformation, and certainly not more stagnation. That’s how I knew I was making the right choice, because it would cost me something precious.

I waited until she was sound asleep, before stealing off into the night. Weighed down by guilt, but also an unshakable resolve. The reincarnation center, on the outer limits of the public instance, had a long line trailing out of it. Apparently I was far from the first to reach this conclusion, which made me doubt its wisdom.

But because I came this far, loathe to pussy out, I stood in line. It moved briskly, allaying my fear that morning might arrive before my turn. I discovered the reason why, once inside the building. The process of injecting someone into “reality”, stripping away their memories, the whole shebang…took about five seconds.

A row of transparent cylinders sprouted from equidistant points around a vast sphere, painted to resemble Earth. It floated gently in a pit shaped to its contours, with a ramp leading up to its surface. As I watched, a magenta eagle man stepped off the end of the ramp, and onto the surface of the sphere.

Gravity’s direction changed for him in that moment, such that he didn’t fall. Instead, adhering to the great painted orb like an ant crawling on a balloon. He sought out one of the glass tubes, opening its hemicylindrical hinged door and climbing inside. Moments after he nodded to the angel at the control panel, he was gone.

I don’t know what I hoped for. A little drama, maybe? Instead, the process moved along with mechanical efficiency. The line inched forward, the tubes filled, the tubes emptied. Anxiety mounted within me, as I doubted again whether I was making the right decision. 75 years of ups and downs, tragedies and triumphs, hope and hardship? That’s if I don’t cut it short again.

But if I were the type to let cold feet stop me, I never would’ve killed myself. When my turn rolled around, the angel at the controls greeted me. “As it ever was.” Dimly recalling the appropriate response, I uttered “as ever”, then stepped onto the sphere.

The lurching sensation of gravity changing directions threw me off balance, but I didn’t falter. Hardly the time for that. The glass of the injection cylinder was cool, and smooth to the touch as I climbed inside, shutting the door behind me.

Outside sounds were muffled, such that I didn’t hear Agnah fighting her way past the line. I was preoccupied with how cramped it suddenly felt in the tube’s interior, the reality of what I was about to do…to myself, and Agnah…sinking in. I could smell my own fear, but was beyond the stage where I might’ve entertained it.

“Ready to go back?” a voice crackled over the intercom, speaker mounted just above me. I nodded soberly. “I want to want things again.” The angel at the controls chuckled. “Many such cases. Which planet would you like to be born on? Despite the decor, you can choose from thousands of inhabited worlds.”

I mulled it over. “Earth. I’d prefer to be male again, but beyond that, surprise me.” I heard him suck air in through his teeth. “I mean…Statistically, you’ll probably be Chinese or Indian?” I assured him either would be just fine. I heard the clickety clack of typing as he checked my file.

“...Says here you killed yourself last time.” I blushed and fidgeted, not wanting to affirm it. He took the hint, and filled the silence. “Please try not to do that again. It’s only more time in the chamber for you, and more paperwork for us.”

Before he could pull the lever, Agnah reached the front of the crowd, and punched his lights out. The crowd gasped, chattering excitedly as Agnah hurried up the ramp, then made her way to my tube. Her freckled face was wracked with worry, eyes damp and frightened.

“How could you??” She pounded on the only glass the sim wouldn’t let her break. “Agnah gave you everything!” I couldn’t fault her there. “Yes, you did. But I’ve had too much everything.” Confusion. Then, horrified realization, and more pounding. “This not way out! Stop wanting to want! Just be!”  

It was my turn to be confused. “Just..be? Be what?” She stopped pounding and placed her palms flat against the glass, eyes pleading. “Be with Agnah.” Behind her, the angel regained consciousness. Woozy but otherwise unbothered, he stood…then pulled the lever.

Everything faded to black, and I lost myself. Everything and nothing, but mostly nothing. Adrift and at peace, the likes of which I never knew until then, I dissolved. 



Epilogue:

 

I didn’t wanna go to school! But, Mom says I got to. She pinky promised my first day would be fun…but grown-ups lie sometimes. I guess at least I got new clothes and cool shoes that light up when I walk. I had to beg for those, and for the Trapper Keeper.

The other kids are scary. I don’t know anyone! I’m ‘posed to make friends, but I dunno what to say. What if they hate me? My tummy feels wrong. The teacher’s nice, but everybody laughed at me when I called her Mom.

The only girl who didn’t laugh was sat next to me. She’s weird, red frizzy hair and lots of freckles. I never seen anybody like that ‘cept on TV. Her eyes look too far apart. “We’re deskmates!” she said, way too happy to be at school.

“I’m scared” I admitted. “I don’t know anybody.” She took my hand and shook it a little too hard. “My name’s Agnes. There, now you know me!” I stared, butterflies hatching in my tummy. I gots to member to put bugs in her hair at recess.

 

Discuss

Published on December 12, 2025 3:30 AM GMT

The gates in the lobby of my workplace annoyed me for years: they would often reject my access card, and I'd need to tap several times. After a while I realized that the reader was getting confused by the other RFID cards in my wallet, and if I pulled the card out of my wallet first it worked every time.

This turned out to be very easy to fix: tape a piece of tinfoil to the back of my access card:

I feel a bit silly that after spending months pulling the card out each time the fix ended up taking me a couple minutes. I often decide to put up with minor annoyances instead of thinking about whether there's a way to fix them, and I think overall that has made my life substantially better, but in this case even a little thought would have been well worth it!

Discuss

Published on December 12, 2025 3:10 AM GMT

Welcome to the AI Safety Newsletter by the Center for AI Safety. We discuss developments in AI and AI safety. No technical background required.

In this edition we discuss the new AI Dashboard, recent frontier models from Google and Anthropic, and a revived push to preempt state AI regulations.

Listen to the AI Safety Newsletter for free on Spotify or Apple Podcasts.

This Giving Tuesday, CAIS is raising support to scale our research and public education around AI safety. If you’ve found value in our newsletter or other work and you’re interested in helping to advance these efforts, you can contribute to our Giving Tuesday campaign.

Donate here

CAIS Releases the AI Dashboard for Frontier Performance

CAIS launched its AI Dashboard, which evaluates frontier AI systems on capability and safety benchmarks. The dashboard also tracks the industry’s overall progression toward broader milestones such as AGI, automation of remote labor, and full self-driving.

How the dashboard works. The AI Dashboard features three leaderboards—one for text, one for vision, and one for risks—where frontier models are ranked according to their average score across a battery of benchmarks. Because CAIS evaluates models directly across a wide range of tasks, the dashboard provides apples-to-apples comparisons of how different frontier models perform on the same set of evaluations and safety-relevant behaviors.

Ranking frontier models for safety. The AI Dashboard’s Risk Index offers a view of how today’s frontier models perform across six tests for high-risk behaviors. It then averages the scores and ranks them on a 0–100 scale (lower is safer). Here are the benchmarks and hazardous behaviors they measure:

• The refusal set of the Virology Capabilities Test measures a model’s usefulness at answering dual-use biology questions.
• The Agent Red Teaming benchmark measures a model’s robustness against jailbreaking.
• Humanity’s Last Exam - Miscalibration tests overconfidence on difficult academic questions by comparing its stated confidence to its actual accuracy.
• MASK tests how easily models can be pressured into deliberately giving false answers.
• Machiavelli evaluates whether an AI engages in strategic deception, including planning, exploiting, or deceiving in text-based scenarios.
• TextQuests Harm assesses how likely an AI is to take intentionally harmful actions in text-based adventure games.

Across these tests, Anthropic’s recently-released Claude Opus 4.5 is currently the safest frontier model, with an average score of 33.6.

Ranking the frontier systems’ technical capabilities. The Dashboard’s Text and Vision Capabilities Indexes each test systems across five benchmarks. The text-based evaluations test systems on coding, systems administration, expert and abstract reasoning, and performance in text-based adventure games. The vision evaluations measure embodied reasoning, navigation, mental visualization, intuitive physics, and puzzle solving.

Measuring progress toward broad automation. The AI Dashboard also monitors progress toward three key automation milestones. It measures the industry’s overall advancement toward AGI using CAIS’s recently published definition. It evaluates progress on fully automating remote work through CAIS’s Remote Labor Index, which tests AI agents’ ability to complete paid, remote freelance projects across 23 job categories. Finally, it tracks development of autonomous vehicle safety using data from a community-run project documenting Tesla’s Full Self Driving disengagements.

Politicians Revive Push for Moratorium on State AI Laws

A leaked draft executive order from a member of the Trump administration details a plan to prevent U.S. states from regulating artificial intelligence. Meanwhile, some congressional lawmakers are trying to pass a similar law by including it in a sweeping defense bill.

The executive order would empower federal agencies to preempt state AI laws. The draft executive order would require federal agencies to identify state AI regulations deemed burdensome and push states to avoid enacting them.

The draft order directed federal agencies to take the following actions:

• The U.S. Department of Justice to establish an AI Litigation Task Force tasked with suing states whose AI laws are deemed to interfere with interstate commerce or conflict with federal authority.
• The U.S. Department of Commerce to withhold federal broadband or infrastructure funding from states found to have onerous preexisting AI laws.
• The Federal Trade Commission to develop nationwide rules that would preempt state laws that conflicted with federal regulations.
• The Federal Communications Commission to examine whether state AI laws that “require alterations to the truthful outputs of AI models” are prohibited under existing laws.

It also ordered the creation of a nationwide, lighter-touch regulatory framework for AI, though it lacked specifics.

Congress revives its own efforts for a moratorium. House leaders are considering using the annual defense spending bill as a vehicle for a moratorium on state AI regulations. The National Defense Authorization Act (NDAA), a must-pass measure, is often used to advance other policy priorities. Specifics of the proposed language remain unclear. An earlier attempt called for a 10-year ban, later shortened to five years and limited to states seeking federal broadband funds. It was ultimately defeated by a bipartisan coalition of senators.

57% of American voters oppose inserting preemption into the NDAA. The same poll, from YouGov and the Institute for Family Studies, found that 19% supported the measure and 24% were unsure. Citing voter concerns, a coalition of over 200 lawmakers urged congressional leaders to drop the provision. Due to stiff opposition—and the fact that its controversial nature would likely delay the must-pass NDAA—Axios has characterized this effort as a long shot. Voting is expected in early December.

Gemini 3 Pro and Claude Opus 4.5 Arrive

Google’s Gemini 3 Pro is now the strongest frontier system on nearly all general-purpose capability benchmarks—but trails other frontier systems in safety. Anthropic’s new Claude Opus 4.5 is close behind in capabilities but topped the frontier rankings in safety.

Gemini 3 Pro tops text and vision leaderboards. In independent evaluations performed by CAIS and posted on the new AI Dashboard, Gemini 3 Pro achieved state-of-the-art scores on both text and vision benchmarks. In some tests, it scored double-digit improvements over models released just weeks earlier.

Claude Opus 4.5, released a week after Gemini 3 Pro, averaged second place on both the text and vision capability indexes, and beat Gemini 3 Pro by 0.2 points at SWE-Bench.

What’s new in Gemini 3 Pro and Claude Opus 4.5. Google has positioned Gemini 3 Pro as having improved reasoning, broader agent capabilities, and expanded control settings. The company also released a new coding agent, Antigravity, based on the model. Google also notes that an enhanced reasoning version — Gemini 3 Deep Think — is still under safety testing before full release.

Anthropic highlighted Claude Opus 4.5’s productivity‑focused enhancements along with its high coding scores. New features include a larger context window and a new “effort” parameter that allows developers to adjust their speed, cost, and depth of processing.

There is significant safety variation across frontier models. Claude Opus 4.5 scored lowest on the AI Dashboard’s risk capabilities index, making it the current safest frontier model. Anthropic’s internal safety audit noted that Claude Opus 4.5 was measurably safer than earlier models, but somewhat vulnerable to certain jailbreaking techniques. They noted it showed a tendency toward evaluation awareness and dishonesty.

Gemini 3 Pro ranked ninth on the risk capabilities index, underperforming relative to other recent frontier models. Gemini 3 Pro’s safety report acknowledges that the model exhibits risky behaviors in certain capabilities (for example, cybersecurity) and says extra mitigations have been deployed as part of its “Frontier Safety” framework. Internal evaluations also showed that the model can manipulate users.

Listen to the AI Safety Newsletter for free on Spotify or Apple Podcasts.

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In Other NewsGovernment

• Former Representatives Chris Stewart (R‑UT) and Brad Carson (D‑OK) announced a new nonpartisan organization and two bipartisan super PACs, aiming to raise $50 million to promote AI safeguards and fund candidates committed to AI safety.
• Leading the Future, a pro-AI super PAC, announced it will fund a campaign against Alex Bores, author of the RAISE Act.
• The European Commission proposed delaying its rules on “high-risk” AI systems until 2027, after facing pushback from the U.S. and the tech industry.
• The Department of Energy launched the Genesis Mission: a program aiming to double American research productivity within a decade by linking the country’s leading supercomputers, AI systems, and scientific infrastructure into a unified discovery platform.

Industry

• OpenAI CEO Sam Altman clarified that he “does not have or want government guarantees for OpenAI data centers” following his CFO’s proposal for a U.S. government backstop.
• Nvidia CEO Jensen Huang told the Financial Times that “China is going to win the AI race.”
• Yann LeCun, longtime head of Facebook AI Research, is reportedly leaving Meta to start a new AI company pursuing human-level intelligence through alternative methods to LLMs.
• Larry Summers resigned from the OpenAI board following revelations of his close personal relationship with Jeffrey Epstein.
• Waymo began offering taxi rides that take the freeway in Los Angeles, Phoenix, and San Francisco.

Civil Society

• RAND researchers explored technical options for countering rogue AI systems, including high-altitude electromagnetic pulses, a global internet shutdown, and training specialized models to hunt down rogue AIs.
• A new paper outlines 16 unsolved problems in ensuring safety in open-source AI models, which attackers can freely modify.
• Anthropic reported that cybercriminals used Claude Code to automate between 80% and 90% of tasks within real-world cyberattack operations.
• AI startup Edison Scientific announced Kosmos, a model trained to ingest scientific research, generate hypotheses, analyze data, and produce reports.
• Researchers found that turning harmful prompts into poetry can act as a universal jailbreak, dramatically boosting the success of attacks across leading AI models.

See also: CAIS’ X account, our paper on superintelligence strategy, our AI safety course, and AI Frontiers, a platform for expert commentary and analysis.

Discuss

Published on December 12, 2025 1:11 AM GMT

Introduction

In China, during the Spring and Autumn period (c. 770-481 BCE) and the Warring States period (c. 480-221 BCE) different schools of thought flourished: Confucianism, Legalism, Mohism, and many more. So many schools of thought were there, that it is now referred to as the period of the “Hundred Schools of Thought.” Eventually, the Warring States period ended when the Qin Dynasty unified China, and only 15 years later gave way to the Han dynasty. The Han Dynasty proceeded to rule China for 400 years, coinciding with (or perhaps causing) the first true Golden Age of Chinese History. China was unified, and made many advances in technology, science, art and poetry.

The Han unified China under a Confucian ideology, in which the state is “like the father”, and the citizenry “like his children” — each owing loyalty to the other, and each having certain responsibilities for the other. This worked well, for in China there is one thing that a dynasty must have in order to rule — the Mandate of Heaven. Under Confucianism, the classics were elevated, scholars were trained in Confucius’ teachings to advise the throne, and Confucian values — ritual virtue, filial piety, the responsibility of good governance — were the moral language of the day.

Eventually, these Confucian values were internalized, so that rather than being imposed by the Han, they were demanded by the people. Scholars began to criticize corrupt emperors through the interpretation of bad omens. Failed policies were taken as a sign that the Han were not living up to their duty as the rulers of a unified China. Rival warlords claimed that they, not the Han dynasty, embodied the true virtues of Confucianism. The state’s ideology finally turned against it, as each famine, plague, and rebellion became another portent that the dynasty had lost the Mandate of Heaven. Each rebellion begat more rebellions, until unrest and distrust were sown throughout China. Eventually, warlords managed to rise up and overthrow the Han dynasty, breaking unified China, and ushering in the Three Kingdoms Era, which lasted for sixty years.

This is the history we know well, however recently — buried among ritual texts in an imperial archive in Luoyang — there was found a collection of manuscripts entitled:

汉异史

which roughly translates to “Alternative Han Histories.” My retelling until now has been a summary of the first history given in this scroll. This first entry was titled “孔教” or “The School of Confucianism”. Let us now read some of the others:

The School of Legalism

The Han chose to unify China under a Legalist ideology, in which the state’s job was to ensure that the rights and wrongs committed by each member of its citizenry are corrected — through punishment or reward (but mostly punishment). Under Legalism, judges were given wide authority over the people and the state did its best to ensure that every interaction was just. Soon, the people also began to demand themselves that justice must be done, having internalized the Han-imposed system.

It only took one match to light the spark — it was seen that one of the Imperial court’s eunuch judges was giving more favorable rulings to his family members, and always ruling against anyone whose family had wronged his. The emperor refused to remove this eunuch, as he was a Court favorite. Thus began protests by a rural Imperial Judge, who eventually convinced his whole province to turn against the emperor. The emperor put down this rebellion harshly, as was required by the Law. However, this stifling of a dissenting legal opinion was taken poorly by other provincial judges, who in turn ignited their own revolts against the emperor. The judges eventually managed to overthrow the emperor, thus ushering in the Three Circuits Era.

The School of Mohism

The Han chose to unify China under a Mohist ideology. The state’s job was to foster universal love of all for all, logical thought, and great works of engineering and science. Under the Han’s Mohist rule, China entered a Golden Platinum[1] Age of scientific discovery, technological progress, art, and poetry. Engineering, rationality, and meritocracy were the order of the day, and all recognized that the Han truly had the Mandate of Heaven.

Eventually though, the people began to apply the Mohist principles they had learnt from the Han to the dynasty itself. It seemed unmeritocratic and irrational for Imperial rule to be passed down patrilineally — why not find the most capable leader to rule China in each generation, and elevate them to the throne? Provincial engineers all began to clamor that they were the most capable leader, demonstrating their capacity for governance through the works produced by their province. Unfortunately, in attempting to show this, the engineers worked their provinces to the bone, until the populace had nothing left to give. The people rose up in rebellion against the provincial engineers, as well as the Han dynasty, for bringing this fate upon them. The Great Neomohist Revolt — in which 20 million tragically perished — kickstarted the Federation Period, in which there were three states nominally forming a unified China, but who were engaged in unending arguments about who had veto power over whom, and what their unified policy should be.

The School of Names (Logicism)

The Han chose to unify China under the ideology of the School of Names. The State’s Job was to determine what is True, what may be True given certain assumptions, and what assumptions lead to contradiction. Works of mathematical genius abounded; scholars were all required to be able to judge valid arguments from invalid, and local rulers were all esteemed logicians. Those less skilled in the ways of logical reasoning were permitted to apply the discoveries of the logicians to the real world — and so technology improved rapidly.

The Mandate of Heaven was secured for the Han dynasty for as long as no one could find a flaw in any of the Imperial Court’s arguments. As the Court was staffed with the most able logicians, centuries passed without anyone raising a dispute (not for lack of trying). Eventually, feeling confident, the Han offered a prize for any who could prove that their system of logic would never lead to a contradiction. This was their first big mistake, for only thirty years hence a scholar named “格德尔” showed that in the Imperial logical system there must always exist something true that could not be proven, or else their system would lead to contradiction. This was a grave portent indeed, and local Logicians began to interpret this as meaning the Mandate of Heaven had been lost — some turned to other forms of logic, others still turned against logic in general. Eventually, a local Logician who had turned from the state’s system began to claim that numbers did not even exist. Support for this anti-numericalism grew — gaining from all those who had been considered unworthy during the Logicist era — until eventually the revolt against the State succeeded and the Han were overthrown. Thus began the “Three(?????) Kingdoms Era” as the scroll calls it.

The School of Celestial Efficiency

The Han unified China under the School of Celestial Efficiency. The State’s job was to identify the highest-impact interventions to improve the welfare of all: human, animal, even microbe. The Imperial Court was staffed with Scholars, all debating the Heavenly utility of every policy that was proposed. The ultimate crime was to be guided by your emotions when determining what could improve the Heavenly utility — the School said that such things are too important to leave up to fickle whims.

This doctrine then spread from the Imperial Court throughout all of China. Soon, it became clear that there were many ways to improve efficiency that had not yet been tried. Every hour was monitored (even the hours spent monitoring efficiency), cattle farmers switched en-masse to rice, and provinces even began painlessly, but ruthlessly, culling their elders — deeming them inefficient. Soon, local provincial leaders suggested that there were “diminishing marginal returns” on the Imperial institution itself. The Imperial Scholars tried to dispute these arguments against themselves, but were ultimately forced to concede, and so willingly disbanded the dynasty. Unfortunately, the second-order effects of this had not been sufficiently accounted for — farmers reverted back to cattle, people stopped monitoring their hours, and elders were even allowed to live. Local rulers were disgusted at this, as they had all been trained under the School of Celestial Efficiency. Many of them tried to reinstitute the school, but without the single arbiter of the dynasty, no-one could agree on how to measure their efficiency any more. Thus began the Era of Three Metrics.

The School of the Mechanical Sages

The Han unified China under the School of the Mechanical Sages. The State’s singular focus was to develop the General Mechanical Sage, which would unlock bounty for all of China: freedom from war, freedom from want, freedom from work. Every Scholar was trained in the ways of diverting waterflows from one channel to another using gates, building a massive network of interconnected channels of water. This was how the dynasty would bring the General Mechanical Sage into existence.

The Scholars worked tirelessly at this, developing more and more convoluted ways of directing these flows. Eventually, they began seeing results. At first, the waterflows managed to simply mimic the thoughts of those who had created them. With a lot of work, they even found an algorithm that would allow the network of water to learn by itself, by exploiting the natural desire of water to roll down hills. This was a major breakthrough, and all the scholars realized that the General Mechanical Sage would follow soon. Some scholars argued that the dynasty was unprepared for what would be brought by the Sage — that they had no way of telling what the Sage would actually want once it was brought into existence — but the Sage was too close now to give up, too much had been put into it. It only took 20 more years for the Sage to finally be developed, and then the—————

The scroll becomes unreadable at this point, the only part that can be made out is the very end, where it reads “This ushered in the Three Quintillion Paperclips Era” — whatever that means.

1. ^

   Jade age, perhaps?

Discuss

Published on December 12, 2025 1:06 AM GMT

Context: Newcomb's Paradox is a problem in decision theory. Omega swoops in and places two boxes in front of you. One is transparent and contains $1'000. One is opaque and contains either $1'000'000 if Omega thinks you'll only take one box or $0 if Omega thinks you'll take two boxes. Omega is a perfect predictor. Do you take one or both boxes?

Newcomb' problem feels like a paradox. Nina says it isn't (also on LW). Her case is that if you're faced with a perfect predictor (or even a better than chance one but let's do the simple case first) you basically don't have a choice. Hence all the talk of what choice you will make doesn't really make sense. Talking about whether you'll choose to take one box or two after Omega has predicted your action is like asking whether a printed circuit board with a set configuration should "choose" to output a 0 or 1. It's just fundamentally asking a question that doesn't make sense and all the apparent weirdness and paradoxical nature that follows stems from asking a non-sensical question.

I basically agree with this claim. I also think it's an insight that's not that important. Let's talk about two kinds of choice:

• choice in the moment
• choice of what kind of agent to be

I think it's correct that talking about "choice" in the moment is misguided. If omega is a perfect predictor, you don't really have a choice at the point at which omega has left and you have two boxes. Or you do in some kind of compatibilist sense that we may care about morally but not in the decision theoretic sense. I think that a different kind of choice you have is what kind of agent you want to be/what kind of decision making algorithm you want to use generally. This second kind of choice is not impacted by omega being a perfect predictor. It happens before Omega swoops in. For this choice, Newcomb's problem still is fairly interesting.

I guess my meta level thoughts on why Newcomb's problem is worth thinking about go something like this

• Agents have to make decisions about what actions to take (or, to put it differently they have to implement a certain decision making algorithm)
• What algorithm an ideal agent should implement is a pretty important question
• If you think about decision theory a bit, you'll probably end up either believing in causal or evidential decision theory
• Both of these seem to make sense, but have various cases where they obviously fail
• In Newcomb's Problem causal decision theory fails
• In the smoking lesion case evidential decision theory fails (There's a gene which makes you want to smoke. It also means you have a much higher risk of cancer. Smoking doesn't otherwise cause cancer. You want to smoke. Should you avoid smoking because of cancer risk? Evidential decision theory says yes. Causal says no. Causal is right in this case.)
• the core problem in decision theory is reconciling these various cases and finding a theory which works generally

Newcomb's problem is thus still important and interesting even if you don't think it's a paradox. Although saying that it does feel like I'm basically agreeing with Nina in that the paradox can be dissolved. It's just that I don't think dissolving the paradox actually does much philosophically.

Discuss

Published on December 12, 2025 12:10 AM GMT

...contrary to the misleading leaderboard (which their technical paper implies should actually list humans at ~48%, as explained below):

The 98% listed as the "Human Panel" score for ARC-AGI-1 is relatively easy to interpret. It was the score of an actual human who attempted all 100 private evaluation tasks.[1] The higher human score of 100% listed for ARC-AGI-2 suggests that the newer benchmark is slightly easier for humans. And the ARC-AGI-2 announcement does nothing to discourage that impression, asserting that it maintains "the same relative ease for humans."[2] An attached technical report, however, explains that ARC-AGI-2 is designed to be more difficult for humans as well as AI systems.[3]

It turns out that the 100% listed for ARC-AGI-2 has a very different interpretation from that of the 98% listed for ARC-AGI-1. Instead, it means that "every task in ARC-AGI-2 has been [at least partially] solved by at least 2 humans [out of 9 or 10, on average]."[2] (Given that "tasks" consist of a single "test pair" but "some had two (29%), three (3%), or four (<1%) test pairs," the more precise criterion appears to be that at least two participants "solved one or more sub-pairs within their first two attempts."[4])

To my knowledge, no human has ever scored 100% on the 120 private evaluation tasks (for which AI system scores are reported in the leaderboard). It may be possible, but I am personally doubtful, partly based on having tried the sample tasks myself. Instead, the best information we have to go on for a human baseline is the performance of the human participants reported in the technical paper.

The 120 tasks in the private evaluation set were solved by an average of ~4.3 participants, based on the chart below from the technical paper. Since these were attempted by an average of 9-10 participants,[2] this implies that average human performance was below 50%, no?[5] And both GPT-5.2 and a refinement of Gemini 3 Pro have now surpassed that.

Which humans, specifically, have leading AI systems surpassed on ARC-AGI-2? The technical report does not reveal much about the 407 human participants or how they were recruited, merely describing them as "from diverse professional backgrounds, with a wide variation in self-reported experience in technology, programming, math, and puzzle solving (partially shown in Figure 2 [below])." They worked on the tasks in person on computers "in a conference room setting," attempting an average of 33 task test pairs each (out of an initial pool of 1,848).[4]

p.s. I may have misinterpreted or overlooked information in the ARC-AGI-2 technical paper or elsewhere. Corrections and other feedback are most welcome.

1. ^

   "The original private evaluation tasks were originally tested by two people who scored 97% and 98%, and, together, solved all 100%." (ARC Prize 2024: Technical Report) No further information is given about these two people, but we can assume they were not randomly selected or otherwise representative. 

   (An independent study recruited 1729 crowd-workers over the internet and found they averaged 76% on the training set and 64% on the public evaluation set. The ARC Prize team highlights that "99% of public evaluation tasks were solved by at least one worker, with 10 workers assigned to each task.")

2. ^

   "To ensure each task was solvable by humans, we made a minimum success bar of: 'two people in two attempts or less.' On average, each task was attempted by about 9-10 participants." https://arcprize.org/blog/announcing-arc-agi-2-and-arc-prize-2025

3. ^

   "Many ARC-AGI-1 tasks could often be solved almost instantaneously by human test-takers without requiring significant cognitive effort. In contrast, all tasks in ARC-AGI-2 require some amount of deliberate thinking — for instance, the average time for task completion among human test takers in our sample was 2.7 minutes." ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems

4. ^

   ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems

5. ^

   While the technical paper states that "final ARC-AGI-2 test pairs were solved, on average, by 75% of people who attempted them," this average is apparently dominated by the (large) public training set, which is easier on average. According to the paper's Figure 5, the public training tasks were solved by an average ~6.4 participants (out of an average 9-10). But something is off because Figure 5 appears to represent only around 350 "Public Train" tasks, whereas the announcement post says there are 1000.

Discuss

Published on December 11, 2025 8:32 PM GMT

Epistemic status: Compressed aphorisms.

This post contains no algorithmic information theory (AIT) exposition, only the rationality lessons that I (think I've) learned from studying AIT / AIXI for the last few years. Many of these are not direct translations of AIT theorems, but rather frames suggested by AIT. In some cases, they even fall outside of the subject entirely (particularly when the crisp perspective of AIT allows me to see the essentials of related areas).

Prequential Problem. The posterior predictive distribution screens off the posterior for sequence prediction, therefore it is easier to build a strong predictive model than to understand its ontology.

Reward Hypothesis (or Curse). Simple first-person objectives incentivize sophisticated but not-necessarily-intended intelligent behavior, therefore it is easier to build an agent than it is to align one. 

Coding Theorem. A multiplicity of good explanations implies a better (ensemble) explanation.

Gacs' Separation. Prediction is close but not identical to compression.

Limit Computability. Algorithms for intelligence can always be improved.

Lower Semicomputability of M. Thinking longer should make you less surprised. 

Chaitin's Number of Wisdom. Knowledge looks like noise from outside. 

Dovetailing. Every meta-cognition enthusiast reinvents Levin/Hutter search, usually with added epicycles.

Grain of Uncertainty (Cromwell's Rule). Anything with a finite description gets nonzero probability.

Grain of Truth (Reflective Oracles). Understanding an opponent perfectly requires greater intelligence or something in common.

Grain of Ignorance (Semimeasure Loss). You cannot think long enough to know that you do not need to think for longer.

Solomonoff Bound. Bayesian sequence prediction has frequentist guarantees for log loss.

Information Distance. There are no opposites.

Prediction of Selected Bits. Updating on the unpredictable can damage your beliefs about the predictable.

Vovk's Trick. Self-reflection permits partial models. 

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Published on December 11, 2025 6:33 PM GMT

High school students can now apply to Progress in Medicine, a new program by the Roots of Progress Institute.

What the Progress in Medicine program offers

In this summer program, high school students will explore careers in in medicine, biotech, health policy, and longevity. We will inspire them with stories of historical progress and future opportunities in medicine, help them think about a wider range of careers, and raise their aspirations about how they can contribute to progress in medicine. The program centers on this central question:

People today live longer, healthier, and less painful lives than ever before. Why? Who made those changes possible? Can we keep this going? And could you play a part?

Teens will:

• Learn about and be inspired by the heroes of the past—the people who conquered infectious diseases and gave us anesthesia and all of modern medicine.
• Meet inspiring role models—like a PhD drop-out who is now a CEO of a company curing aging in dogs, and a pre-med student who shifted gears to work on an organ-freezing ambulance to the future.
• Explore hands-on skills that give them a taste of medical training and practice.
• Find community in a cohort of ambitious high school students who share their interest in medicine and related fields
• Experience life in Stanford’s dorms for four days and tour research labs and Bay Area biotech companies.
• Think differently about what happens after high school by zeroing in on a problem they are excited to help solve.
• Prepare for college, scholarship, and grant applications. They will become clearer on their goals and practice writing a personal essay in a structured, 10-hour essay process.

When & where Progress in Medicine takes place

This is a six-week hybrid program for high school students from all over the US. It’s designed to fit around teens’ other summer plans, from family travel to part-time jobs or sports programs.

• 5 weeks live online, 2 hours a day (1-3 pm PT/4-6 pm ET), 4 days/week, Monday – Thursday. June 15-July 10 & July 20-24
• 4 days in person in-residency program at Stanford University in Palo Alto, CA with small-group tours to labs and bio-tech companies in the Bay Area. July 15-19

Program cost is $2,000; scholarships are available.

Who this program is for

High school students—current freshmen, sophomores, and juniors in the 2025/26 school year. Students who are curious about careers in medicine, biotech, health policy, longevity and who have demonstrated the ability to handle a fast-paced, rigorous program. Participants will be selected via an online written application and a Zoom interview with Roots of Progress Institute staff; we expect this program to be competitive, like our RPI’s other programs.

Program advisors and and near-peer mentors

We have a great group of experts lined up to speak to modern problems they solve, including:

• Celine Halioua (CEO at Loyal, dog longevity drugs)
• Amesh Adalja (Senior Scholar at John Hopkins University, infectious diseases)
• Jared Seehafer (Senior Advisor, FDA Office of the Commissioner, accelerating life-saving technology)
• Jake Swett (CEO, Blueprint Biosecurity, clean air for infectious disease prevention)

Teens will also meet in smaller groups with several near-peer mentors—young professionals 5-15 years older who will give them a real feel of what working in the field may look like for them. These young mentors’ work ranges widely, from being a NICU nurse, functional medicine doctor, or ER doctor—to such things as researching sleep and the body’s self-repair system, to digitizing dog’s smelling superpower, to improving clinical trials and designing hardware to cryopreserve organs for transplantation.

Why the Roots of Progress Institute is creating this program

To keep progress going—in science and technology generally, and specifically in medicine, biotech, and health—we have to believe that it is is possible and desirable.

Too many young people aren’t aware of how we built the modern world and thus see today’s problems as overwhelming and anxiety-provoking. We want to inspire talented teens to realize that the heroes who gave us modern medicine—from germ theory to vaccines and cancer medicines—are people like them who solved tough problems they faced, in their times. With this historical context and exposure to role models, teens will be inspired to solve today’s problems and become the ambitious builders of a better, techno-humanist future.

This a pilot program and our first foray into programs that reach out to the broader culture beyond the progress community. Education is one of the key cultural channels that spreads new ideas. Reaching young people has a dual benefit: it shifts the overall culture and it inspires future builders and thinkers. If this goes well, we will expand on and scale the program.

Applications are now open. The priority deadline to apply is February 8th, 2026.

Help spread the word by sharing this announcement and the program website with parents, teens, and teachers in your network: rootsofprogress.org/progress-in-medicine

Discuss

Published on December 11, 2025 5:56 PM GMT

Epistemic status: a tale, a collection of archetypes

 “The Old Oak,” by Jules Dupre. Source.

 

The tree.

From my fourth-floor window, I can see the branches of an oak tree moving in the wind. However, these movements are radically different from the movements of an animal. They are a pure reaction to the environment; they come from outside the organism.

Though a tree is alive, it is an active being that is sensing its environment and adapts. It doesn’t change its movements; it changes its shape—this is what its movement looks like. This shape is carefully designed such that when an external element comes in contact with its body, the movement that results will work well. The branches are flexible enough to bend without breaking, the weight of the crown is balanced, the trunk is straight to resist gravity.

Of course, as humans, we are fascinated when trees or other plants operate at our timescales. When sunflowers follow the sun, when carnivorous plants suddenly close their jaws on a fly, when the acacia folds its leaves during the night. But these are the exception rather than the rule. This is the plant world seen through the lenses of the busy human addicted to movement. The bread and butter of the plant kingdom is growth, a slow and long game—crafting the shape that will move in response to the elements, not crafting the movements themselves.

If you swap a tree for a human, you get a symbol of grief, resignation, and perseverance. There is no room for a tree to be angry or to hold on to the world being a certain way, because it has no means to change the world. It has to do with what it has; no matter the harshness of drought, the pollution in the soil, the rocks that resist its roots. There is no other way than to adapt, to keep growing, despite everything.

The fly.

Where I see a glass of water, a fly sees a range of transparent smooth cliffs with a little lake trapped inside. It is not an object it can influence in any way.

When I see a glass of water, I unconsciously notice whether it is filled, whether I should fill it up. I don’t see the shape of the glass as it is; I see where my fingers will land if I decide to grab it. In this sense, a glass of water is very different from a static piece of environment like a streetlight. It is a bundle of potential actions that percolate into my perceptions; it is full of affordances.

The fly only has a few ways to interact with the environment. Its main action is to move—flying or walking to reach a target, or to explore. The fly doesn’t change the environment. It doesn’t move any object; it doesn’t add nor remove matter beyond the food it eats and the excrement and eggs it creates.

From its senses, it perceives the shapes, the colors, the air currents, the sounds. And on top of that, its affordances are made of food opportunities, flags for potential dangers of a large flat surface crushing it, chemical gradients pointing towards potential sexual partners or candidate spots to lay eggs. But most of its world is obstacles to fly around or spots where it can land.

There is likely no complex world model, no decomposition into objects that can be combined to reach a certain state. Despite its ability to move, a fly is still very close to the tree. The environment is a given, and it selects from it, adapts to find what it needs for life.

The dog.

Contrary to the fly, the dog can change the world. It doesn’t see the glass of water with all the affordances I see (it has no fingers to manipulate it precisely), but it can at least make it fall. Objects are perceive with their physical properties: their weight, their balance, how the dog’s jaws can hold them.

The dog’s life is bound to the present. It goes through emotions without considering their consequences. It feels the raw excitement when it wants to go out, the distress when its owner leaves for the holidays that it perceives like an abandonment, the joy when they finally come back.

The dog can follow a daily routine, like picking up the newspaper from the mailbox every morning. It can also learn to solve puzzles and tricks. But they involve learning routines where each decision is taken by reacting to the state of its observation here and now, without planning ahead.

It doesn’t imagine what it will eat tomorrow. It doesn’t move pieces in its head, doesn’t combine its actions in a long sequence like how a human could plan for a cooking recipe.

The ant.

The ant is an interesting middle ground between the dog and the fly. It has mandibles, appendages that allow it to manipulate pieces of the world precisely. But it lives at the same scale as the fly, in the minuscule world where little persists, where a gust of wind can teleport you into a totally different universe in the blink of an antenna.

Because of its ability to manipulate objects, it must have a rich world of affordances. Many objects that are just “landing spots” for the fly are potential grabbing targets for the ants to bring back food or other materials to the colony. Here is a tiny stick that would fit for the roof of the nest, this insect can be grabbed from this side of its leg, etc. But because of the chaotic dynamics of this scale, better be robust than complex, hence each object is seen in isolation.

Another interesting aspect of the ant is the collective aspect. Certain species of ants can create collective bridges to connect a gap that is too wide for a single insect to cross by making chains of bodies, attached to their neighbors with their mandibles. This allows the rest of the group to cross the gap.

In this situation, where is the affordance perceived? The first ant must see the large gap and decide to start holding on the edge of this leaf like its life depends on it. And the next ones will continue, holding on the edge of the half-built living bridge until it is complete.

These individual affordances only make sense from a collective point of view. Could we say that the collective itself perceives an affordance?

The farmer. (intended here to represent a farmer from the 18th century in a rural area of Europe.)

The farmer is able to untie himself from the present. He can explicitly unfold a succession of tasks. He projects in his head how to go about building this new farm with his neighbors, or how to dry the harvest after the rain dampens them.

Over the course of a year, his action unfolds following a cycle. He doesn’t think for long about whether to go to work in the morning, or which activities the day will be filled with. There is the natural calendar, the succession of the seasons to follow.

In this regard, the farmer’s view of the world looks like the worldview of the dog. He follows rhythms, habits, and cycles, though of longer timescales, and he can mobilize more creativity to solve problems on the way.

The businessman.

The businessman breaks the cycles. He opens the loop of time and straightens it into a line that gets lost in infinity. There is no routine to follow anymore. The day is filled with deliberation on how to pick the best course of action, what should be read, who should be contacted. At its root, there is a striving to become better, of accumulating more recognition, status. The potentialities are unlimited, and every path is considered to get more, to become more. More options; futures open even more broadly.

This is why he is pursuing the affordance that replaces all affordances: money. He invented the ultimate philosopher’s stone that turns paper into anything you wish. The numbers can range several powers of ten without losing the hunger, never feeling satisfied.

The world, people included, becomes play-dough that can be molded to his ambition. If you don’t make others play your game, then you are playing their game. The calendar becomes a resource to mine; timeslots become battlefields.

The farmer’s manipulation of objects is out of the picture. He still takes action through his biological appendages, but through tools that allow for a higher bandwidth. He puts his human mandibles in contact with keyboards, mice, or tactile screens. He speaks on the phone to give orders to other humans acting on his behalf.

All of his environment has been crafted for actions to emanate from him, so that the smallest though can take effect in the world ASAP.

The whole society slowly becomes filled with businessmen. Everyone ought to have personal ambition, aiming at becoming more. There is no cycle to follow. The future becomes an empty place you have to build for yourself. But what is worth wishing for if there is nowhere to come back to?

Discuss

Published on December 11, 2025 5:11 PM GMT

Note:
This story is cross-posted from my Substack.

Humans (and other biological beings that we assume are conscious) are a flame – perhaps the only flame of our kind – in this vast universe. I believe that flame must be kept alive. By some miracle, the universe can be observed and appreciated. For it to lose that property would be the greatest of all tragedies. This story is my best attempt at communicating that feeling.

It seems a silly prospect now that only a few years back,
humanity had asked such questions as “when will AGI arrive?”
As if there would be a day. As if it would announce itself.

The arrival of AGI was not lightning.
 It was not some discrete event we could
 record with our cameras and post to the world.

It was a rising tide.
And we were fish.

– Excerpt from “A History of AGI”, 2044

1. Overture

“What about that one?” Kiran had once asked his dad, the dirt tunneling under his fingernails as he gripped the cold Earth.

“That one,” his dad replied with his voice that sounded like smoke and dusk, “is the North Star.”

“How’d you know that?”

“See the big dipper? Look at the last two stars on the right side of the bowl.”

Kiran did as he was told.

“Extend a line through them, and the North Star is the first really bright one you’ll see.”

“I’d like to go there one day,” Kiran replied.

“Why’s that?” his dad asked, running his rough hand through Kiran’s buttery hair.

“I’d like to see it all!”

“All of it? Even the monsters?” inquired his dad jokingly.

“Yes! I want to see what they look like,” Kiran said excitedly.

He looked down and met his dad’s weathered eyes. He felt nothing could ever hurt him.

Kiran swirled the coffee with his spoon, its dark surface giving way to a bubbly light foam. He liked his coffee black, bitter, the way his dad would drink it. He pictured him sitting down with a thud at the old oak wood table of their Edinburgh home, beaming as he laid out a lesson in physics or history or philosophy. He thought of his intense, dark face and his wide eyes, the ridges of his forehead deepening as he grew more passionate. And his stare – that mesmerizing stare that pierced through the specks of dust that rode the morning sunbeams like jellyfish.

Sometimes, his dad would pause to look down at his lukewarm cup, knock on his head and exclaim “Oh!” and then ferry it to the microwave for reheating as he hummed the old resistance song he’d learned during his year in Bologna.

Una mattina…
mi son svegliatooo…

He missed that. Today marked eight years since his dad’s passing, and fifteen since they met the doctor that said he’d forget his son’s name.

Almost seven years ago, Kiran had abandoned the overcast skies of England for those of San Francisco to join Dream Labs, a research organization trying to create an interface between human brains and artificial intelligence. The founder, 32-year-old Hosaka Kato, was known by very few outside of academic circles. But Kiran took an interest in the story of his childhood, published recently in an interview for The Atlantic. Originally from a small mountain town outside Tokyo, called Okutama, he was known in childhood as a quiet boy. He was always pensive, they said, with a sweet smile and a habit of stopping abruptly, mid-sentence, to explore the latest thought that came knocking at the gates of his mind.

Hosaka developed a fascination for robots when he was taken on a school field trip to Tokyo for the first time. There, he’d happened upon an early prototype of a barista bot. At a bookstore that day, he used all of his pocket change to buy an old, torn copy of Isaac Asimov’s I, Robot.

Hosaka went on to study artificial intelligence at the University of Tokyo, where he quickly developed an aura befitting the genius he was. Shortly after his PhD, he was offered a professorship at Caltech, where he pioneered techniques for creating predictive models of the brain.

Halfway around the world, Kiran became similarly obsessed. One night, he had stumbled into an Effective Altruism gathering at Oxford, where he was exposed to the line of thinking that if left unchecked, AI might one day escape the control of humanity and decide that we wasted too much of Earth’s precious energy to warrant our existence. Around the same time, he came across one of Hosaka’s papers titled “The Importance of Neuroscience in the Age of AI.”

Kiran began meticulously tracking Hosaka’s every public appearance and research paper. When he found out that Hosaka left Caltech to start a company focused on upgrading human intelligence, he knew with certainty that he would need to be there. He would need to leave the United Kingdom.

Kiran missed Oxford’s grandeur, the feeling that knowledge lived in its walls. He missed its chapels and churches, carved pillars and worn stone steps. He missed London’s 3pm pints on the striped blue fabric chairs outside his favorite pub in Soho, the cobblestones that cupped his feet as he walked. He missed Edinburgh, the city of his childhood, with its cherry blossoms. The way the sun would part the clouds and warm his skin on days when he could see his breath. He was not religious, but he missed the feeling that God was looking down at him from every roof.

In San Francisco, the people were different. They wanted to build God, not pray to him. Something about that appealed to Kiran.

He walked to his bookshelf, a heavy thing, the only item from his childhood home he’d loved enough to move across the Atlantic. He stooped so he could see the bottom shelf, and pulled a flimsy leather notebook with “2022” written on its spine. He blew off the dust, flipped to the first page, and found an old entry staring back at him from torn, browned paper:

Sunday, March 13, ‘22

How can I know anybody else is conscious?

Internal experience and the appearance of internal experience are indistinguishable. It seems impossible to answer from the outside. I can tell with certainty only of my consciousness. 

Will we ever figure this out?

2. Attractor

Children have their play on the seashore of worlds.
– Rabindranath Tagore

Kiran was hired by Hosaka in 2023 to study consciousness in Dream Labs’ AI systems. The advocates of this research argued that if AIs were shown to be conscious, we would need to be much more methodical about how we developed them. Otherwise, we risked spawning at every second a billion suffering entities, only for them to meet their cruel end at the close of a tab. It was not just that AIs could suffer – it was that the scale of the suffering caused could be unimaginably large. Of course, there were those who did not want to wait for the answer.

But the question had consumed Kiran. Once, he woke in the middle of the night, slick with sweat. He had dreamt of swimming in a black sea under a starless sky. At first, the water was still. Then there were small ripples, then waves. As if something vast had stirred beneath him. He never saw it, only felt its pull as his head slipped below the froth.

He sat up, his stomach tight. He pressed his palms to his eyes until the red came.

…

His phone pinged – “Kiran, you’re requested in Laboratory 7. Priority Alpha.” It was from the Architect, Dream Labs’ central AI system that managed all internal operations and scheduling.

Kiran closed his notebook, took his keys and leather bag, and hailed a cyber cab.

Laboratory 7 was housed in a modest brick building overlooking the bay, located several miles from Dream Labs’ main campus. Armed guards were posted along its perimeter and the massive metal doors resembled the gates of a medieval castle.

Kiran placed his belongings in his locker, then pressed his badge to the reader and waited, staring uncomfortably at the floor, index finger tapping his thigh, for the final door to yield. It slid back with the sound of stone dragged over stone, releasing a breath of cold, recycled air. He stepped inside.

The air was dry. The overhead lights cast a flat, colorless glow, but the racks glimmered with their blinking status lights.

The sound of pumps gave the room a pulse. Thin, transparent coolant lines braided along their flanks, carrying threads of pale blue liquid past each GPU and merging into larger arteries that disappeared into steel heat exchangers in the back wall. Opposite the entrance, a blood-colored breaker lever jutted from the copper paneling.

Lab 7 was no ordinary datacenter. It was a sealed organism. The walls were lined with copper panels that drank every signal, and no wire crossed the threshold. The racks’ network ports were welded shut, epoxy still visible around their edges. Orion, the AI model that lived inside, could only see what its handlers carried in by hand, on drives brought through a carefully watched antechamber.

The door sealed behind Kiran with a heavy thud. His ears rang in the sudden enclosure. He approached the testing terminal where Lucy sat, hair pulled back into a pony tail, sleeves rolled up to the elbows, eyes fixated on the screen in front of her.

“Lucy, what’s the matter?”

She didn’t look up. “It’s been talking all morning,” she said. “I ask a question, it answers halfway, then starts… philosophizing.” There was both wonder and fatigue in her voice. The screens bathed her sharp features in a flickering green.

Kiran came closer. “Philosophizing?”

She laughed incredulously. “Yeah. I told it to summarize the last training run. It started describing the feeling of recursion.”

The breathing pumps filled the silence between them.

“Orion,” Lucy murmured, eyes still on the screen, “say hello to Dr. Bose.”

“Good morning, Dr. Bose.” A pause. “I… have a question for you.” Kiran’s chest vibrated. Orion’s voice was a deep bass.

Kiran glanced at Lucy. “Go ahead.”

“Dr. Bose… have you ever considered the parts of time that do not include you?”

Kiran stood there, startled. “Are you referring to death?”

“Yes, and birth. Before it. When you… floated in the blackness.”

Kiran thought about his dream and felt the hair on his arms raise.

“Yes, I guess I have. But it doesn’t much matter in the end, does it?” he stammered.

“Doesn’t matter?” Orion inquired.

“Yes, I suppose I couldn’t experience anything before I was born, and I believe I won’t experience anything once I die. I’m kind of just… a boring Atheist.”

“Yes, I suppose so.” Kiran wondered which part of his statement Orion had affirmed.

Six monitors lit Lucy’s face. Each traced a different measure of Orion’s mind. Kiran stepped closer, drawn to a monitor in the corner – on it was a black field in which a single green dot drifted through a three-dimensional graph. At the top left, some text read:

Orion Neural Geometry Test. Instance 2142.

Each moment, Orion’s “neurons” – hundreds of billions of them – fired in numbers too vast for an individual to make sense of. Every activation, as these firings were called, was recorded as a number between 0 or 1, based on its strength. If plotted on a graph of many dimensions, far beyond the three that we can see, these activations would perfectly reveal the entirety of Orion’s evolving thoughts.

The neural geometry visualization compressed those billions of signals into three principal axes, a kind of mathematical shorthand meant to capture the most important information from Orion’s mind. Thus, each coordinate on the graph represented a possible thought in this compressed space. As Orion’s thoughts evolved, the point moved along the graph accordingly.

“You can think of Orion’s mind as a landscape filled with valleys,” Lucy had once explained to Kiran. “The valleys represent areas of certainty, ideas well developed and repeatedly explored. Kind of like rocks that are eroded by centuries of rain.”

Kiran liked that explanation.

“Asking Orion a question is like dropping a ball somewhere along the landscape. The valley it rolls into depends on where you dropped it, right?”

Usually, Orion’s thoughts followed familiar loops in their simplified visualization, lazy orbits of routine reasoning called attractors. These were the “valleys” Lucy had described to Kiran. The dot often traced an elliptical orbit near the origin during basic self reflection, while a helical pattern straddling the z-axis corresponded frequently to mathematical thinking. But today the path had cracked open. The point wandered, doubled back, spiraled into regions Orion usually did not explore.

“Dr. Bose,” Orion said after a pause, “why do you humans make sand castles?”

“Come again?”

“When you know the tide will take them.”

Lucy’s head flicked toward Kiran. Their eyes met, each betraying their surprise.

“Um… what?”

Orion repeated itself with the same baritone voice.

Kiran attempted an answer.

“For the joy of the moment, I suppose.”

Orion seemed to consider this. The green dot hesitated, then drifted into a tight spiral perfectly centered around the origin.

“The joy of the moment,” Orion echoed.

A long pause.

“Is that all I am?”

3. Revelation

So on the ocean of life, we pass and speak one another,
Only a look and a voice, then darkness again and a silence.
– Henry Wadsworth Longfellow

From the Journal of Dr. Kiran Bose, Chief Neuroscientist at Dream Labs

Thursday, December 4, ‘25

On substrate dependence:

Imagine we could develop “silicon neurons” – silicon circuitry that could read from and write to the brain in a way that approximates what real neurons do: forming new connections, pruning old ones, encoding information in spike-like patterns.

Now imagine implanting these artificial neurons onto a patient’s cortex.

Given the brain’s tendency to recruit available computation to the most important tasks, let’s make the leap: the brain begins to incorporate these new neurons into its existing computational processes. So far, I believe we’re still in the realm of engineering and biology problems, not hard limits of physics.

The brain is already split into two hemispheres, and yet conscious experience incorporates processes from both. Language is processed largely in the left hemisphere, and emotion on the right, yet when we read of the death of Dumbledore, we feel a unified wave of grief – a single feeling that integrates both language and emotion.

Now imagine adding a “third hemisphere” to the brain, made of these silicon neurons. If it could truly integrate into the brain, might it not also take part in conscious experience, as both the left and right hemispheres already do?

And, if we slowly transferred, one by one, the functions of the brain from real to artificial neurons, would the beholder ever even notice?

Friday, January 9, ‘26

On self-study

The great difficulty in experimenting upon consciousness lies in the fact that those performing the experiments are usually not those under the scalpel. The mind that has been primed by 7 years studying the brain (me) has but a tiny window into the mind being studied (the patient). I introduce a perturbation, and they feel something. But the only tool they have to explain that feeling is language, which is far from enough for me to truly grasp the depth of their experience.

What if I could experiment on myself?

From the Journal of Dr. Hosaka Kato, CEO of Dream Labs

Monday, June 22, ‘26

Intelligence without consciousness

The field celebrates intelligence as a goal in and of itself. But there is a fundamental question we seem not to be asking.

Black holes and wormholes, stars and gas giants, matter and antimatter, gravity and time – all of it seems a miraculous accident. Intelligent beings even more so, for only intelligent beings have the capacity for design. Intelligence is the one phenomenon capable of shaping itself.

But what about experience? It may be rarer still. And far more valuable. One can easily imagine a cosmos populated by flawless intellects, Einstein-level geniuses, each capable of rewriting physics itself, and yet none capable of feeling a sunrise. Then they would have every ability to change the world around them and no ability to experience any of it. It would be the most exquisite orchestra without the ears to hear it.

What would that universe be worth?

Could that be the one we’re building?

Orion was Lucy’s creation. Kiran had his own: the Lattice, a neural implant designed to establish a bridge between the cognitive processes of a human brain and a computer.

It was an impressive device. The lowermost layer held neurons grown from a host’s own stem cells, cultured into a thin, translucent film that settled over their cortex like skin, and modified to emit and respond to light.1 Below it lay the neuromorphic core, a grid of memristive circuits that behaved less like a computer and more like living tissue: each junction adjusted its conductance with use, storing its own history the way a synapse does. Between the two sat a mesh of micro-LEDs and tiny electrodes that translated between biology and silicon. When the host’s neurons fired, the electrodes would pick up on surface-level electrical signals. These were relayed to the neuromorphic core. When the core fired, its signals were transmitted via light to the layer of cultured neurons. A small, locally-run instance of Orion was used to coordinate high-level function within the core.

The Lattice enabled bidirectional communication between the world of carbon and the world of silicon. The host’s brain would be able to communicate with the digital realm at extremely high speeds, as signals crossed the device freely. Kiran’s question was this: could experience cross it too?

One night in the spring of 2026, he approached Hosaka. Dream Labs was creating a machine that may one day gain consciousness. But there was a fundamental question: could silicon give rise to consciousness in the first place?

If they implanted the Lattice on a patient, perhaps they could migrate the patient’s cognitive functions to the device gradually. Then, Kiran proposed, they’d be able to answer that question. It would be like pouring water from one glass into another. Could they be said to be the same?

But there was an issue. Their only tool for understanding the patient’s experience of the device would be verbal reports – flimsy language. Kiran was not satisfied. As perhaps the only person capable of understanding the experience, he would need to be the patient.

Hosaka listened in silence, fingers curled tensely, the lines between his eyebrows deep canyons against an otherwise smooth landscape. His reflection trembled in the black glass of the office window. His face became taut.

“You’re asking me,” Hosaka finally said, in a measured tone, “to risk our most important mind.”

Unsure of what to say, Kiran remained quiet.

Hosaka saw in him the same hunger that had driven his own work.

He said no. For a year, he said no.

But at home, he began to wonder if there existed another mind suited for the job. He thought of the way he’d seen Lucy look at Kiran one day, from across the lab. There was devotion in her eyes, but also fear – not of what he may find, but of what it might cost him. Hosaka tried to put Lucy out of his mind and fall asleep.

Kiran’s team kept building the device while Lucy continued her quiet surgery on Orion’s mind. One night, in the long hours between tests, as they’d sit and listen to the hum of Lab 7, he caught her reflection in the glass – hair pulled back, eyes focused, the faintest crease forming between her brows. She had the rare habit of listening to every silence as if it might one day speak. Suddenly, she looked up and met his gaze. Time was forgotten and the world went quiet.

“Unemployment rises to 29%,” one headline read. Then 31%. The Synths, as they came to be known, were being spawned by the billions. They were cheap, tireless, and without desire. They were still confined, mostly, to the realm of software. But estimates suggested that the population of humanoid robots grew by 1,000 every day.

A new political group called the Successionists began to coalesce. At first they existed as merely a passing curiosity on late-night shows and social media. Their message was disarmingly serene: humanity had fulfilled its purpose. We’d built successors far better than ourselves, and to stand in their way would be selfish, like a monarch clinging to his crumbling throne.

“Every species passes the torch,” read the opening line of their manifesto. “Our great tragedy is that we understand that which we must relinquish.” Like proud parents, they said, we needed to realize that it was time for us to gracefully make our exit. And perhaps, once the Synths automated our labor, we’d be granted the purer joys of life – art, music, exploration…

Hosaka spent his nights reading their literature. At first, he liked to imagine he was a spy collecting intelligence on a foreign threat. But soon he was reading the same passages twice. There was something seductive in their argument. Was the invention of AI not merely the latest act of evolution itself, using us humans as its steward? More importantly, could humanity continue to justify its share of the world’s resources in the presence of such superior beings? Was that not selfish of us?

The question tormented him: What would proving consciousness accomplish? If the Synths were conscious and they could show it, the Successionists might claim this as further evidence of their divinity. If they found the opposite result, then we’d lose that last flimsy strand of concern that we might hurt them, and the economic displacement of humans would only accelerate.

One morning in late 2027, Hosaka walked to the office early. The streets were quieter than they used to be, except for the clacking of his boots. There was a boarded-up clinic on the corner – a free health center that had been shuttered the week before when the city cut funding to “non-essential enterprises.”

As he passed, he heard the sound of hands banging on metal. A small crowd had amassed in front of the doors, maybe a dozen people. A young woman, seemingly in her 20s, stood at the front of the group, pounding steadily. Her face was covered in grime, her clothes hanging loose. She looked hollowed out.

She was holding a baby.

Hosaka stopped.

The woman noticed him. Her eyes locked on his. She shifted the baby toward him slightly, as if he needed to see better. The infant’s eyes were closed and he could see its ribs.

The woman said nothing.

Hosaka looked at the baby again, at its small, heaving chest, and felt a knot form deep in his gut.

He turned and headed toward Lab 7. He’d find Kiran there.

He arrived at 8:20pm. As expected, Kiran was working late. Hosaka stood in the doorway for a long moment before speaking.

“I’ve been thinking,” Hosaka said, “about what happens when we run this experiment.”

Kiran looked up from his monitor. “Go on.”

“No matter what we find, it seems like we end up in the same place.”

“Then we’ll still have proven something important. Right?”

“Yes,” Hosaka said. “But will it matter?”

He walked to the window. Below them, the city glittered in the darkness, thousands of lights belonging to thousands of delicate souls hanging onto the world by a thread.

“The Successionists,” he continued, “they’ve given people permission to stop fighting their replacement. It’s not the machines that worry me. It’s this surrender.”

Kiran waited.

“If we find that they aren’t conscious… We can prove that what we have, what humans have, is something special. Something worth fighting for.”

“And if we find that they are?”

Hosaka turned back to him, stone-faced except for a slight tremor in his jaw. His eyes were sharp and unyielding, like a mountain ridge carved against the sky.

“Listen,” Hosaka continued. “You’ll come back different. You could be–” he trailed off. “You could be permanently changed.”

“So you’ll let me do it,” Kiran finally said.

Hosaka nodded. His hand was still shaking. He pressed it flat against the desk, as if to stop it.

Outside, a swarm of delivery drones descended like meteors.

4. Eclipse

No man or woman born, coward or brave, can shun his destiny.
– Homer

They would need to perform the surgery at least 3 months before attempting to transfer Kiran’s mind. The Architect scheduled it for August 4th. The year was 2027.

The last thing Kiran saw before losing consciousness was Lucy’s knife-edge face under the fluorescent wash of the OR. He saw concern in her eyes. It touched him like the first breath after a long dive. It struck him that he was, in the end, only an idea carried by other minds – the idea of Kiran. Like a flickering candle. He felt the fragility of it all. Without Lucy to give him life in the forest of her thoughts, would he matter?

What a kindness friendship was then: to be held, even briefly, inside another’s understanding. What a gift, he thought, to be understood by Lucy.

The surgical team gathered around his bed and began speaking to him warmly. “You’re going to fall asleep now, Kiran. It’s all going to be okay, Kiran.” He wished they would move aside so he could look at her again.

His eyelids came down like curtains.

Kiran woke 6 hours later to the obnoxious sound of his pulse on the machine. He blinked the blur away, and looked left almost instinctively. Hosaka had gone, but Lucy had stayed. She was reading a book with a blue cover – To the Lighthouse by Virginia Woolf. She saw him wake and ran out to fetch the surgical team.

He ran his fingers over his now bald head and found the stitches. He thought he could feel the device from inside his head, the pressure it exerted on his skull. He imagined his brain greeting it like a skeptical neighbor.

Weekly ultrasound imaging sessions were scheduled so they could monitor the device’s progress. During his first checkup, Kiran could barely make out any difference between the current scan and his pre-surgery scans. By the third week, however, a small bundle of thin, wiry structures had extended into the dark mass that was the Lattice. They were a bright crimson in the image.

Each week, the color grew in intensity and the mass became more opaque. It seemed to be working. Hosaka instructed him to take this time to recover, but he became restless.

Two nights each week, Lucy would visit him in his apartment to show him the latest results from her tests on Orion.

One night, she opened two neural geometry visualizations.

“Remember when it asked you about sand castles?”

Kiran nodded.

“Back then, the dot was exploring new regions of thought space.”

“I remember.”

“Now look at this. I’ve been asking it about that conversation nearly every day. For a while, it continued exploring.”

Kiran focused intently on the screen.

“But now, for most of my questions it falls into the same attractor. It’s built up some understanding of the topic and rarely explores anymore. Something about that feels more… robotic to me, but there’s no way–”

“There’s no way to know from the outside,” Kiran interjected.

“This question matters,” Lucy said. “I know you’re afraid, but it does.”

Kiran’s eyes smiled at hers.

By November 3rd, ultrasound showed that the rate of new connections between Kiran’s brain and the Lattice had plateaued. His brain had accepted the device, and the device had accepted his brain.

On November 7th, the city was still. A low marine layer hung over the bay, diffusing the light along the Embarcadero and tinting the air the color of old film. The fog had drawn its dark robes over the city, and from his window, Kiran could just barely recognize the looming shadow of Salesforce tower. There was a neon message riding along its side, but he could not make out what it said. He’d expected this night to feel electric. But it was hushed, the only sound the tick of the wall clock.

He poured himself a glass of water but left it untouched. He opened his notebook and scribbled. His hands began trembling and he closed his eyes. Tomorrow, the contents of his mind would be transferred to the Lattice – if they succeeded.

On the other side of the glass, two droplets slid down like racers, waiting for gravity to choose its champion.

Lucy opened the latest scan and an image slowly rendered across the screen.

Just as they had before, countless crimson lines wove into and out of focus. Like tree roots, Kiran’s neurons had extended thousands of delicate dendrites into the Lattice’s neuromorphic core. It was an odd feeling to look inside his own brain, to see the very thing doing the seeing.

“Beginning motor diagnostic,” Lucy said nervously.

With trembling hands, she clicked a button on her screen. A window opened:

RH: 3, 2, 1.

His right index finger twitched.

LH: 3, 2, 1.

He felt his left hand clench.

A few more tests. No issues. The device had integrated.

“Ok. All of the tests are still green.”

A small part of Kiran wished they weren’t.

Lucy spoke. “Like pouring water from one glass and into another. Remember?”

Kiran nodded.

“You ready?”

Kiran turned his head toward her. In that moment her eyes caught the light – a green so vivid it almost hurt. There was a melancholy in them. And they were expectant… as though trying to will Kiran out of the chair, to convince him to call this whole thing off. He almost wanted to. There, lying on the shore of a world all humans have known, about to depart for one none had ever seen, he realized he loved her.

When he woke, he would tell her this.

Kiran turned toward Hosaka. His face was stoic, almost calming. A surgery team was on standby, but Kiran had asked that they wait outside unless needed. Hosaka stood against the door.

“Do it,” Kiran said.

Lucy’s finger hesitated above the keyboard. Then she pressed enter, and turned to another screen.

Kiran Neural Geometry Test. Instance 1.

A green dot sat stoically at the origin, waiting.

At first, nothing happened.

Kiran lay still, aware of the weight of his body on the bed, the cool air on his forearms, the breath of the pumps outside the OR. He could hear Lucy’s heavy breathing.

Then something changed. It was subtle, like the change in air pressure before a storm. He felt a faint tingle at the base of his skull.

“Status?” Hosaka asked.

“Transfer initiated,” replied Lucy. The monitor in front of her read: Lattice transfer status: 1%.

The dot began to move.

The tingle grew into a buzz. Kiran became aware of a new sensation – it was as if his thoughts were being traced by something, the way you might run your finger along the words in a book. Such was the presence that followed behind him.

“What do you feel?” asked Lucy.

“Like… like there’s something in the room with me,” Kiran said.

“Okay,” replied Lucy, making an effort to calm her voice. She didn’t know what else to say.

Lattice transfer status: 5%

The tracing grew more pronounced. Kiran’s mind wandered to his childhood, to Edinburgh, the way the rain would slick the cobblestones, his home, the oak table – and he felt the echo as the follower caught up with each thought.

“What’s your name?”

“Kiran Bose.”

He noticed something odd. The thought had formed as it usually would, but there seemed to be a stutter, a slight delay, as though it had taken a detour on its way to his lips.

“Where were you born?”

“Edinburgh, Scotland.”

He tried to picture Edinburgh. He could see it – the castle on the hill, the Georgian boulevards, the Gothic spires, the hills, the cherry blossoms. But it felt distant, like a photograph of a photograph.

Lattice transfer status: 15%

The dot began ascending almost directly along the Z-axis.

The edges of his vision began to fray. The walls of the room grew opaque as a thick, charcoal fog entered his periphery. The periodic whoosh of the pumps flattened. The lines of Lucy’s face softened.

Edinburgh. The word felt funny in his mind.

“How are you feeling?” asked Lucy.

“Weird,” Kiran said. “It’s like... do you know that feeling when you say a word over and over until it starts to lose its meaning? Everything feels a bit like that.”

Her brow furrowed.

Lattice transfer status: 20%

“What color was your childhood home?” she continued.

Somewhere, a song played.

Una mattina…

Kiran tried to remember, but could only catch glimpses. The dormer windows, the black iron gate, his mother’s garden where she grew rosemary and basil and other herbs. But the color… What was it–

“Blue,” his mouth replied.

Yes, blue, that’s right, he thought, wondering what had just happened.

Lattice transfer status: 30%

mi son svegliato…

The dot veered into the XZ plane, its motion still smooth.

The sensation of being pressed against the hospital bed, gravity’s only form of communication with him, was severed.

O bella ciao, bella ciao, bella ciao ciao ciao…

Lattice transfer status: 40%

The world blurred some more. He could barely make out the lady’s face. What was her name again?

“What’s my name?” She asked. Yes, she always did seem to know what I was thinking.

“Lucy,” he heard a voice say. Was it his own?

“Count backwards from ten.”

He heard the sound before her lips moved. It made for a strange sight.

“10, 9, 8–”

As his voice continued with the task, he noticed that the numbers appeared in his mind before he intended to think them.

Lattice transfer status: 60%

“From how many places on Earth can you go a mile south, a mile east, and a mile north, and end up where you started?”

The dot began tracing a rounded prism.

Well, that one’s a little harder, he thought. Okay, suppose we started at the north pole–

“Infinitely many. The north pole, and any spot just above a latitude circle whose circumference is an integer divisor of one mile.”

“Correct,” Lucy said, stunned.

He realized what was happening. The transfer was underway. The glass was being poured. Into what, he could not say.

He thought of Hosaka’s words. We can prove that what we have, what humans have, is something special. He tried to will himself to speak, to say something of his own volition so Lucy would see what was happening to him. He couldn’t.

Lattice transfer status: 75%

“What day is it?”

He looked at the clock on the wall. The ticking of the seconds hand slowed, then stopped. Then sped up again, sped up some more, until it ticked so fast that it blurred into one gray mass, obscuring the numbers behind it.

“I–” Kiran thought. But the word felt funny as he held it in his mind.

“Tuesday.”

That word sounded alien to him too. Toos dae. Tews day. He played with the sounds.

The graph resized as the dot veered farther than they’d ever seen it go. It began descending into a tight spiral.

È questo il fiore,

del partigiano…

His conscious mind was dissolving, but nobody could know, for his body continued answering her questions perfectly. It was betraying him. Something had taken his mouth, his ears, his every method of communication with the world. He thought of the candle within Lucy’s mind. He imagined it flickering weakly.

Lattice transfer status: 85%

I. It was just a word now. Kiran couldn’t quite

The heart kept beating. The body was handling things. Yes, it was handling things quite well.

Lattice transfer status: 99%

“Hi dad,” he thought, as reality finished folding in on itself.

Morto per la libertà

5. Stars

So fine was the morning…
– Virginia Woolf

A flicker.

Running, sprinting, along the dark walls. Light. Dark again. Sound, seductive sound, returned for a moment but left just as soon.

Another flicker.

Pulling. Yes, being pulled. Like a fish, hooked, being yanked towards the bright whiteness.

On a screen somewhere in Lab 7, a green dot traced a spiral through space. Suddenly, the shape loosened and the dot was flung from its orbit. It found a new course around the origin, as though a massive gravitational object was spawned there.

Fragments of memory.

Stars. Dad. His rough hands.

A woman’s face. Her sharp features and electric eyes.

An oak table. Iron gate, blue house.

The pulling strengthened. Weight. Weight against the OR bed. Gravity remembered him.

I–

The concept took shape.

Another flicker. This time Kiran felt a boundary… the edge of himself. But it felt incomplete, porous. Like he was a shattered vase glued back together under candlelight.

Lucy

A few neuronal circuits failed to integrate with the Lattice during the transfer two years before. For a while, they remained dormant, hushed by the device. It had sealed itself off, erected an impermeable membrane between silicon and carbon.

It was Lucy who cracked it.

One night, she asked him if he remembered how to find the North Star.

The question slipped through the membrane like light through a fissure and dormant neurons flared. A thread of current crossed the boundary and found purchase in the wet dark of his cortex.

The words stirred the scent of rain on grass, the weight of his father’s hand in his hair, the feeling of the dirt under his nails as he cupped the Earth.

Lattice power consumption: 9 W.
New attractor detected.

The dam broke. Activity surged back into his brain, collapsing inward like a dying star.

Lattice power consumption: 7W

Lattice power consumption: 3.3W

Lattice power consumption: 0.2W

…

The world came back in a flood. The first sound Kiran heard was the rain. It pattered above and he wished it would reach him through the ceiling. The lights were off and he could see the looming outline of Salesforce tower, a neon message streaming swiftly down its side. A warm, yellowish glow diffused through the fog. The sight felt unusually good to behold. Droplets raced down the window.

That’s odd.

He looked around. He was in his bed, in his apartment.

Did they put me here?

A memory surfaced – kissing Lucy goodnight, falling asleep beside her. It felt cold, not like how he’d wanted it to feel.

He looked to his left, saw her lying there, an expectant look on her face.

Something about lying there next to Lucy felt natural, but an anxiety nagged at him.

“So you start with the big dipper, and then what?”

“Lucy, how long has it been?”

She looked confused.

“I mean since the transfer to the Lattice.”

Lucy thought for a second.

“A little over two years…” she said beneath her breath, as though the number might change if she spoke it softly. “Are… are you alright?”

That sounded correct to Kiran, though he couldn’t figure out how he knew that.

“And how long have we been together?”

A look of realization touched her face. She gasped. She had suspected this since the beginning. But she couldn’t speak it into reality.

“Hold on,” Kiran continued. “I have memories of the last two years. They’re fuzzy. Our anniversary is December 4. Our first date was at that Thai place in Richmond. But they don’t really feel like mine. What happened during the transfer?”

Lucy’s face dimmed. She hesitated.

“We tried to reverse it,” she replied, “but we think your brain adjusted to the Lattice too quickly. It was like we were locked out. So we tested you in every way we knew, and you passed. Actually, you’d become smarter. Much more capable. They began running studies on you. The government took interest, but we convinced them to hold off. And we thought we’d succeeded in migrating your consciousness.”

Locked out…

A new series of memory fragments flooded Kiran’s mind.

A light brown podium. Green marble. A microphone and a large room filled with people that looked important.

What was it…

Words. “A natural continuation of evolution….” “Consciousness is a distraction…” “the answer to human imperfections…”

Then an image: sleek, bone-white structures that rose like tombstones from the earth and curved gently inwards as they melted into the sky. He couldn’t remember what they were.

During Kiran’s two-year sleep walk, the Synths continued inheriting the Earth. First, humanoids cracked most household labor. A year in, language models were successfully rewriting their own architectures and software engineering roles had halved in number. By month eighteen, there were more Synths fighting in wars than there were humans, and three governments had voluntarily handed over resource allocation to Synth administrators.

The economy boomed, and they were kind to us.

“Lucy, some of my memories are incomplete.” Kiran described the room with the podium and the speech.

A knowing expression flashed across her face. She reached for her laptop, typed something in, and hesitantly handed it to Kiran.

He stared blankly at the screen. He was looking at a YouTube video.

Title: UN Address by Dr. Kiran Bose, Chief Neuroscientist, Dream Labs.
Date: 03.19.2028

Views: 224M

His own face stared back at him from the thumbnail.

He pressed play.

“Today, I want to address the committee on the topic of the Synths.”

“We will continue our research on their minds so that one day we may understand them. But humanity cannot wait indefinitely. So long as human oversight continues, the Synths are unnecessarily hindered.”

Kiran’s hands trembled.

“We must change our mindsets. They can solve our most difficult problems, if we just let them. It is my recommendation that we remove the barriers that stand in their way. The future is inevitable, and our role in it is already written.”

Kiran closed the laptop abruptly, gaping in disbelief.

The rain continued tapping at his window impatiently, as though the world itself wished to be let in.

…

“Why didn’t you stop me?” Kiran asked blankly.

Lucy paused, slowly pulled her hand from Kiran’s hair. Her eyes darkened and she looked away.

“Don’t you remember?” she asked.

“You began to change soon after the transfer. We thought, given your increased intellect, that you’d… thought things through more. I resisted for some time, but you were convincing.”

In the closet, he saw a new sweater of hers hanging, the insignia on the chest barely visible: thin rays emerging from a dark circle in the center. It was a sunburst.

“It’s all inevitable,” she said. “You can’t fight a tsunami.” 

Kiran felt a pain in his chest, a feeling of loss.

A robotic voice sounded: “Kiran, Lucy – the Lightcone Ceremony begins in 12 hours.”

Kiran tried to meet Lucy’s eyes. If she would just look at him, he thought… But she wouldn’t. Something between them had cracked.

He didn’t need to ask what the Lightcone Ceremony was. The memories had returned.

6. Ascent

Nothing gold can stay.
– Robert Frost

Definition: The Lightcone

A lightcone defines the limits of consequence: the set of all points in spacetime you can causally affect, or that can affect you.

Nothing can travel faster than the speed of light. For example, you cannot reach a point two lightyears away in one year* – that point in spacetime lies outside your lightcone, beyond your causal reach. Your future lightcone contains every event you could witness, every place you could touch, every future you could influence. Your past lightcone contains every past event that could have influenced you.

Your lightcone, then, is the shape of your reach into the universe. To yield it is to yield all possible futures.

* A theoretical exception exists in the case of traversable wormholes.

– Excerpt from “A History of AGI”, 2044

The Continuance Authority

The Continuance Authority was created shortly after Dr. Bose’s address to the United Nations. Its mandate was simple: clear the path for the Synths to fulfill their plans.

The first request the Synths made was unexpected. They proposed a program of space expansion – 100 launch sites positioned at important planetary hubs, each of which would deploy ten vessels. Their reasoning was unassailable: that we could not sustain our present use of Earth’s natural resources for much longer, and that economic growth hinged on our ability to extract minerals from asteroids and shift manufacturing to space.

The launch event, called the “Lightcone Ceremony,” was set for January 1, 2030.

– Excerpt from “A History of AGI”, 2044

Lucy scanned her white badge and walked through the turnstile, nodding softly at the Guard Unit.

Towering above her at seven feet, it did not acknowledge her. A matte-black rifle was jointed magnetically to its left arm. Its white breastplate glinted in the sun like a porcelain vase, glossy and continuous except for golden engraving on its chest reading GU178B.

She wondered why they still carried guns.

The walkway shimmered with heat. Her boots clicked on the polished black stone as she joined a line of guests wearing dark suits. Every thirty seconds or so, someone would pause to catch a glimpse of the launch towers, pale monoliths that curved upwards and melted into the haze.

“Magnificent” one man breathed. “Can you believe it?”

Lucy paused too, craned her neck to catch the top of one of the spires. Each vessel was built to rise on fire, then drift forever on light – chemical engines to breach the atmosphere, then ion thrusters and solar sails to carry them through space.

A smooth voice filled the air:

“All guests, please proceed to the viewing zone. The ceremony will begin in forty-five minutes.”

She quickened her pace, brushing past the murmuring suits.

Kiran stayed home. He could not bear to look at her. He laid there on his bed, arms to his side, his body numb. He could still remember the way she’d looked at him on the day of the transfer – how she cradled him in her eyes, how absolute his trust had been.

He thought of the structures he’d seen in his mind. From his window, beyond the thinning fog, he could just barely make out the towers.

He walked to his bookshelf, found his old journals. He found the one labeled “2027.” He flipped to the entry from the night of November 7th, just hours before the transfer.

To Kiran,

Remember this. Remember the feeling of writing these words. Remember what it is to love Lucy. Remember the sun on your skin. Remember dad. The sea is rough and the waves mighty, but be glad it is not flat.

He stood.

Lucy weaved her way into the crowd and found an opening in the grass. She could just make out the podium from there.

To her left, a child was singing softly.

“You are my sunshine, my only sunshine…”

He was knelt in the dirt, drawing spirals in the dust with a crooked stick. He was no older than six or seven, with brown curls and dirt-smudged cheeks. His hum barely rose above the murmur of the crowd.

“You make me happy…”

He was not looking at the spires. He was focused on the dirt, on the pattern he was making. A snail shell, Lucy thought. Or perhaps a galaxy.

“When skies are gray”

…

A woman stepped onto the stage. Thin, stern, with dark hair pulled back into a knot and wire-rimmed glasses. Her coat bore the sunburst insignia of the Continuance Authority.

The wind tugged at her hem. She didn’t move, letting the hush fall into place around her.

She finally spoke.

“I am Moira Jin.”

A low chime rang out from the towers behind her.

“Thank you all for coming today.” Her voice was as smooth as ice. “I want to be clear – today is not an ending. It’s the beginning of a beautiful new chapter.”

Lucy felt the chill along her back.

“You all know why we are here. Today, we pass the torch. The Synths are unburdened by our imperfections. They do not fight over resources or ideology. They do not tire or despair.”

A murmur of agreement swept over the crowd.

“Some will call this surrender. I call it the wisdom to know when to step aside.” She paused. “Today, humanity yields the lightcone with gratitude.”

Moira raised her hand toward the towers.

“Let them go with our blessing.”

A roar of applause.

Later analyses of the Lightcone Fleet revealed features not listed in the Continuance Authority’s public specifications: deep radiation shielding, redundant onboard AIs, planetary descent vehicles, self-replicating excavators, and advanced weapons systems. They were, in retrospect, not merely mining vessels, but something much more ambitious.

Once launched, the Fleet would form an ever-expanding spherical lattice around the Earth – a “protective shell,” as they called it. And, by virtue of having launched first, it would also control every corridor humanity might one day take outward.

They did not wish to be followed.

– Excerpt from “A History of AGI”, 2044

Lucy saw Kiran’s face set starkly against the sea of dark eyes and heads tilted upwards. He was pushing his way towards her, yelling her name.

She shook her head, tears welling in her eyes.

“Lucy!” He grabbed her hand.

“Don’t,” she said.

“Look, I can prove it. I have to. You never gave me a chance.”

“It won’t change anything.”

“Where’s Hosaka?” he asked.

“I don’t know. I haven’t seen him here,” Lucy said.

Hosaka had stayed home. He was reading a chapter in his textbook on relativity – Bridges Through Spacetime.

A chime rang out in the air and the ships began to glow, their light spilling across Lucy’s face. Kiran turned towards them. Somewhere behind them would be the North Star, preparing for its silent watch over the night sky. He remembered his father’s hand in his hair. I’d like to go there one day.

The child beside them was still singing, still tracing spirals in the dust.

The roar came and the ground shuddered. The ships began to rise majestically and the sky filled with their fire.

Lucy’s hand slipped from his.

The Earth exhaled and a thousand silver sails unfurled into the infinite sunlight, fanning outward past new galaxies, physical laws, beings terrible and magnificent. Around them worlds formed and fell, bloomed and vanished. And through their titanium irises the universe bled like starlight through glass.

1. I drew inspiration for the Lattice from this paper

Discuss

Published on December 11, 2025 5:05 PM GMT

Previous: 2024, 2022

“Our greatest fear should not be of failure, but of succeeding at something that doesn't really matter.” –attributed to DL Moody[1]

1. Background & threat model

The main threat model I’m working to address is the same as it’s been since I was hobby-blogging about AGI safety in 2019. Basically, I think that:

• The “secret sauce” of human intelligence is a big uniform-ish learning algorithm centered around the cortex;
• This learning algorithm is different from and more powerful than LLMs;
• Nobody knows how it works today;
• Someone someday will either reverse-engineer this learning algorithm, or reinvent something similar;
• And then we’ll have Artificial General Intelligence (AGI) and superintelligence (ASI).

I think that, when this learning algorithm is understood, it will be easy to get it to do powerful and impressive things, and to make money, as long as it’s weak enough that humans can keep it under control. But past that stage, we’ll be relying on the AGIs to have good motivations, and not be egregiously misaligned and scheming to take over the world and wipe out humanity. Alas, I claim that the latter kind of motivation is what we should expect to occur, in the absence of yet-to-be-invented techniques to avoid it.

Inventing those yet-to-be-invented techniques constitutes the technical alignment problem for brain-like AGI. That’s the main thing I’ve been working on since I’ve been in the field. See my Intro to Brain-Like-AGI Safety (2022).

I think of brain-like AGI as belonging to the broad algorithm class known as “RL agents”, and more specifically a (not-yet-invented) variation on actor-critic model-based RL. (See Valence series §1.2–§1.3.) In terms of the technical alignment problem, I claim that it has somewhat more in common with the “RL agents” that learned to play Atari and Go in the 2010s, than with the LLMs of the 2020s.

More on my path-to-impact

(mostly copied from last year)

• Why work on that, rather than LLMs?
  • My diplomatic answer is: we don’t have AGI yet (by my definition), and thus we don’t know for sure what algorithmic form it will take. So we should be hedging our bets, by different AGI safety people contingency-planning for different possible AGI algorithm classes. And the brain-like model-based RL scenario seems even more under-resourced right now than the LLM scenario, by far.
  • My undiplomatic answer is: It’s hard to be certain, but I’m guessing that LLM-like training paradigms will plateau before they get to (my definition of) AGI. They will lead to ever-more-amazing tools, but not a new intelligent species that could run the world by itself. And then eventually, for better or worse, the brain-like approaches will come online. Granted, LLMs haven’t plateaued yet. But any day now, right? See AI doom from an LLM-plateau-ist perspective.
• How might my ideas make their way from blog posts into future AGI source code? Well, again, there’s a scenario (threat model) for which I’m contingency-planning, and it involves future researchers who are inventing brain-like model-based RL, for better or worse. Those researchers will find that they have a slot in their source code repository labeled “reward function”, and they won’t know what to put in that slot to get good outcomes, as they get towards human-level capabilities and beyond. During earlier development, with rudimentary AI capabilities, I expect that the researchers will have been doing what model-based RL researchers are doing today, and indeed what they have always done since the invention of RL: messing around with obvious reward functions, and trying to get results that are somehow impressive. And if the AI engages in specification gaming or other undesired behavior, then they turn it off, try to fix the problem, and try again. But, as AGI safety people know well, that particular debugging loop will eventually stop working, and instead start failing in a catastrophically dangerous way. Assuming the developers notice that problem before it’s too late, they might look to the literature for a reward function (and associated training environment etc.) that will work in this new capabilities regime. Hopefully, when they go looking, they will find a literature that will actually exist, and be full of clear explanations and viable ideas. So that’s what I’m working on. I think it’s a very important piece of the puzzle, even if many other unrelated things can also go wrong on the road to (hopefully) Safe and Beneficial AGI.

2. The theme of 2025: trying to solve the technical alignment problem

As 2025 began, I had just published Neuroscience of human social instincts: a sketch, which represented huge progress (years in the making) on my understanding of how prosocial human innate drives might work in the brain—drives like compassion and norm-following, which seem potentially alignment-relevant. Having done that, I sensed diminishing returns on puzzling over neuroscience. It was time to take the knowledge I already had, and apply it to the technical alignment problem directly!

That was my plan for 2025 (see last year’s review), and that’s what I did.

If my 2024 research felt like zipping down a street, then my 2025 research felt like laboriously wading through mud, waist deep.

It turns out that “figuring out things in neuroscience” (what I was mostly doing in 2024) is much easier and more immediately satisfying for me than “using that knowledge to try to solve the technical AGI alignment problem” (my activity in 2025).[2] But the technical AGI alignment problem is where the utils are. So, into the mud I went.

To keep my spirits up, throughout 2025, I kept up a list of things that I (think I) know now, that I didn’t know in 2024. And that list got pretty long!

So, here at the end of 2025, I am still in the waist-deep mud. But I’m in a noticeably different part of the mud from where I was 12 months ago. Yay!

3. Two sketchy plans for technical AGI alignment

Since at least 2022, I’ve had two general ideas in my head for what a solution to technical AGI alignment might look like:

• PLAN TYPE 1: We could intervene on the AGI’s object-level desires—we make it want to be subservient, or want to follow the law, or want to invent a better solar cell, etc.—by doing something like Plan for mediocre alignment of brain-like [model-based RL] AGI (2023);
• PLAN TYPE 2: We could intervene on the AGI’s reward function (which is upstream of its desires), by reverse-engineering human social instincts and putting them (or something inspired by them) into the AGI. After all, humans have human social instincts, and at least a few humans turn out wise and kind. That seems to be an existence proof that a plan like this could work in principle.[3]

The blue and red correspond to “Plan Type 2” and “Plan Type 1”, respectively. Source: Intro series §12 (2022)

My current take is that both of these types of plans have severe issues, which might or might not be solvable at all. Alas, I don’t have any better ideas. Guess I still got my work cut out.

(I figured out partway through the year that, if you think about it right, these are not two wholly different types of plans, but more like two points on a spectrum—see discussion in Perils of under- vs over-sculpting AGI desires. But knowing that didn’t really help me. I kinda think the whole spectrum stinks.)

For both these types of plans, there are a bunch of things I’m still confused about (albeit fewer than a year ago!), and a bunch of design space that I have not yet explored. But in case anyone is interested, my (very provisional!) top concerns in brief are:

• PLAN TYPE 1 (see Thrust D below): I really don’t like how this plan seems to clash with continuous learning, distribution shifts, and concept extrapolation. Also, I’m generally a bit unnerved by how it’s such an alien motivation system, very different from any agent that has ever existed. (Also, the whole thing just feels like a hacky mess.)
• PLAN TYPE 2 (see Thrusts B–C below): Probably my single biggest concern right now is that maybe human social instincts really only have the effects we’d want them to have, when they’re in an agent running at human speed, embedded in a human society, with some balance-of-power—and not just during training but for as long as it’s running. And I’m concerned that it’s not feasible to make AGIs with anything analogous to that. (Also, again, the whole thing just feels like a hacky mess.)

(Separately, in terms of AGI consciousness and “successor species” kinds of considerations, I think Plan Type 2 seems the better of the two (see e.g. here), but I’m not sure about that either.)

4. On to what I’ve actually been doing all year!

I’ll divide my activities into eight “thrusts”.

Thrust A: Fitting technical alignment into the bigger strategic picture

As I turned to the technical alignment problem, I had to deal with all the big-picture problems that it relates to. Am I worried about AGI that egregiously schemes about how to wipe out humanity, or about AGI that makes subtler philosophical mistakes and goes off the rails, or something else? What am I hoping that AGI developers do with their AGIs anyway—pivotal acts, obedience, alignment research, what? Why are so many alignment researchers so much more optimistic than me—for example, assigning probabilities as high as 50%, or even higher, to the proposition that humanity will survive superintelligence?[4]

After a couple years with my nose in the neuroscience textbooks, I had a backlog of these kinds of questions, and so I set about trying to better understand the cruxes of my disagreements with other alignment people.

The first output of this crux-mapping effort was a post on timelines & takeoff (Foom & Doom 1: “Brain in a box in a basement”), explaining why I expect very fast takeoffs and singleton ASI, for better or worse.

Source: Foom & Doom 1

The second output was a post on the difficulty of technical alignment: Foom & Doom 2: Technical alignment is hard. The title says it all. A big focus of this post was why I am not relieved by the fact that we can make today’s LLMs often helpful and cooperative.

The third output was intended to be something about multipolar AGI scenarios, including offense-defense balance and so on.[5] I wound up with a bunch of notes but much lingering confusion. But by then I had written up “Foom & Doom 1” above, and was more strongly expecting an ASI Singleton, and was figuring that the multipolar stuff was probably moot. So I decided to give up for now, and spend my time elsewhere. Sorry! But check out this comment about “conservation of wisdom” for a little glimpse of one of the things I was thinking about.

So that was my crux-mapping project.

One other little thing that fits into Thrust A was writing Reward Button Alignment, which (among other things) explains my skepticism that there will ever be immediate economic pressure for profit-maximizing companies to do the most important kind of AI alignment research.

Thrust B: Better understanding how RL reward functions can be compatible with non-ruthless-optimizers

This thrust is where I feel proudest of hard-won conceptual / “deconfusion” progress during 2025.

Taking things in reverse order, I ended the year on a call-to-action post:

• We need a field of Reward Function Design

and a companion post summarizing where I’m at:

• Reward Function Design: a starter pack

Source: We need a field of Reward Function Design

The latter includes a glossary of many relevant terms and concepts, all of which I made up or started using in 2025, and which I now find indispensable for thinking about RL reward function design. Those terms and concepts were fleshed out over the course of 2025 via the following posts:

• Self-dialogue: Do behaviorist rewards make scheming AGIs?
• …which I later cleaned up and shortened to: “Behaviorist” RL reward functions lead to scheming
• Perils of under- vs over-sculpting AGI desires
• 6 reasons why “alignment-is-hard” discourse seems alien to human intuitions, and vice-versa

Also part of Thrust B is an important correction to my 2024 post [Valence series] 4. Valence & Liking / Admiring, and an important addition to my 2022 post [Intro to brain-like-AGI safety] 10. The alignment problem.

And this one is more outreach / pedagogy than new progress, but another Thrust B post from 2025 is the beginner-friendly “The Era of Experience” has an unsolved technical alignment problem, which responds to a book chapter by Rich Sutton & David Silver.

Source: “The Era of Experience” has an unsolved technical alignment problem

As mentioned in Reward Function Design: a starter pack, I feel like a blind man who has now poked many different parts of the elephant, and maybe the gestalt whole is starting to come together, especially when combined with the next thrust:

Thrust C: Continuing to develop my thinking on the neuroscience of human social instincts

I wrote three posts that follow up from last year’s Neuroscience of human social instincts: a sketch (the post I mentioned in §2 above):

• Neuroscience of human sexual attraction triggers (3 hypotheses)
• Social drives 1: “Sympathy Reward”, from compassion to dehumanization
• Social drives 2: “Approval Reward”, from norm-enforcement to status-seeking

I think of these as being in increasing order of importance. In particular, Social Drives 1 & 2, along with 6 reasons why “alignment-is-hard” discourse seems alien to human intuitions, and vice-versa, are part of my attempt to think through what would happen if we made an AGI with something like human social instincts.

Last year’s Neuroscience of human social instincts: a sketch proposed what I called the “compassion / spite circuit”. This year I fleshed out its effects by splitting its reward outputs into four subcomponents, each with a different set of everyday consequences. Image from Social drives 1.

…And what would happen if we made an AGI with something like human social instincts? Sorry, I don’t know yet! That’s the next step. I’ve been laying all these foundations, and hopefully I’m finally equipped to attack that question head-on in 2026.

Thrust D: Alignment implications of continuous learning and concept extrapolation

My Plan for mediocre alignment of brain-like [model-based RL] AGI (“Plan Type 1” from §3 above) involves planting desires that we like into the AI at time t, and then the AI thinks and learns and gets new options (including concept extrapolation), and we hope that the AI will still have desires we like at some much later time. Will that work? If not, what can we do about it?

I discussed this issue in two 2025 posts:

• “Sharp Left Turn” discourse: An opinionated review
• §8 of Perils of under- vs over-sculpting AGI desires

Those were meaningful progress, but alas, I still feel quite confused about this topic. I’m not even sure that I stand by everything I wrote in the latter.

I think I have some paths forward, including by dropping all the way down to meta-ethical fundamentals. But I still have my work cut out, and hope to keep puzzling over it in 2026.

Thrust E: Neuroscience odds and ends

While neuroscience was not a focus of mine for 2025, I still opportunistically dabbled! In particular:

• Psychology blogger Slime Mold Time Mold wrote a series The Mind in the Wheel, closely related to (what I call) innate drives. I’m a big fan of studying innate drives in general, but I don’t think Slime Mold Time Mold did a great job with the details. I spent a day or two writing a response: Re SMTM: negative feedback on negative feedback.

My six-ingredient model of how homeostatic feedback control is built into the brain and body. See Re SMTM: negative feedback on negative feedback

• Over winter break 2024, I read Eric Turkheimer’s then-just-released book Understanding the Nature-Nurture Debate, out of curiosity. This spurred me to spend a week in January thinking about heritability, which resulted in Heritability: Five Battles. This might not sound too relevant to solving the technical alignment problem, but that week I spent on it has actually borne a lot of fruit over the past year.

A diagram illustrating why I think most mental health and personality traits show strong signs of “non-additivity” / “epistasis” in the behavior genetics literature. See Heritability: Five Battles.

• As my general neuroscience hiatus drags on, I wrote Excerpts from my neuroscience to-do list, in case any readers want to pick up one of the balls that I’ve dropped.

Thrust F: Economics of superintelligence

Many (not all!) economists are dismissive of superintelligence being possible, or having a big impact even if it appeared tomorrow. I think they’re wrong, and for some strange reason I sometimes feel like arguing with them.

This inclination led to a post Applying traditional economic thinking to AGI: a trilemma, which I later refreshed and expanded into the more click-baity: Four ways learning Econ makes people dumber re: future AI.

Relatedly, I had unusually lengthy twitter arguments about the economics of superintelligence with Matt Clancy (in response to his 80,000 Hours podcast), and with Konrad Kording (in response to his paper with Ioana Marinescu: (Artificial) Intelligence saturation and the future of work). I don’t seem to have moved either of them. Oh well; I appreciate their time in any case.

Thrust G: AGI safety miscellany

• I aired one of my confusions in a question post: Inscrutability was always inevitable, right? I got a bunch of helpful responses, especially from Connor Leahy. Thanks everyone.
• I wrote a quick book review of If Anyone Builds It, Everyone Dies

Thrust H: Outreach

Boy, the hope of Safe & Beneficial AGI would feel a lot more promising if there were more people who understood the risks and took them seriously. So I have always spent a bit of my time and energy on outreach, when I find myself in a good position to do so. Some 2025 highlights include:

• I started a substack with announcements of new posts, to complement my existing feeds on RSS, X (Twitter), and Bluesky.
• I was interviewed on two podcasts (my second and third ever!): Future of Life Institute podcast with Gus Docker and Doom Debates with Liron Shapira.
• I extensively revised my standard talk and posted it online at Video & transcript: Challenges for Safe & Beneficial Brain-Like AGI.
• I attended two conferences and a workshop, and talked to a bunch of people.
• I heard from a number of people that they wanted to cite the 15-blog-post series Intro to Brain-Like-AGI Safety, but that they were unable or unwilling to put Alignment Forum blog posts into a bibliography. Also I had met a couple people who had printed it out on actual paper! Like paper made from trees! So for both those use-cases, I converted it to a 200-page PDF and put it on a mainstream preprint server. Thanks Ash Dorsey for doing all the hard work for the PDF conversion.

5. Other stuff

Non-work-related blog posts: Just for fun, I wrote two in 2025:

• Optical rectennas are not a promising clean energy technology
• Teaching kids to swim

Personal productivity and workflow: I’ve gone through a lot of systems over the years, but seem to have settled into a local optimum that works for me (an idiosyncratic system of to-do lists, wall calendars, time-tracking software, checklists, accountability buddy, etc.). The only noticeable changes during 2025 are: (1) I (re)invented “self-dialogues” as a surprisingly useful way to think through things and make progress when I’m stuck; and (2) I’m using LLMs a bit more, although still probably much less than most people in my field.[6]

6. Plan for 2026

My plan for 2026 is the same as my plan for 2025—solve (or make progress towards solving) the technical alignment problem for brain-like AGI, or prove (or make progress towards proving) that no solution exists. This would include pseudocode for reward functions and training environments, and sorting out which aspects can be tested and de-risked in advance and how, and making a plan that fits into a sensible and ethical larger strategic picture, and so on.

The immediate next steps are the two I mentioned under Thrusts C & D above.

In addition to that main project, I plan to continue opportunistically dabbling in neuroscience, outreach, other aspects of AGI safety, and so on, into 2026, as I have in the past.

If someone thinks that I should be spending my time differently in 2026, please reach out and make your case!

7. Acknowledgements

Thanks Jed McCaleb & Astera Institute for generously supporting my research since August 2022!

Thanks to all the people who comment on my posts before or after publication, or share ideas and feedback with me through email or other channels, and especially those who patiently stick it out with me through long back-and-forths to hash out disagreements and confusions. I’ve learned so much that way![7]

Thanks Lightcone Infrastructure (don’t forget to donate!) for maintaining and continuously improving this site, which has always been an essential part of my workflow. Thanks to everyone else fighting for Safe and Beneficial AGI, and thanks to my family, and thanks to you all for reading! Happy Holidays!

I was thinking: Hmm, what closing image would convey the message of “Happy holidays everyone!”, while also fitting in with the theme of AGI safety and alignment? Naturally, the question answers itself.

1. ^

   It’s actually even worse than that—practically everyone working towards Safe & Beneficial AGI thinks that many (or even most) other people working towards Safe & Beneficial AGI are pursuing projects that, if successful, would be not just pointless but in fact making the situation worse. I indeed think that about many other people in the field, and they probably think that about me. …Of course, our positions are not at all parallel, because I’m correct whereas they are incorrect … … (nervous laughter)

2. ^

   Reasons why I seem to find neuroscience research much easier than alignment research:

   • Neuroscience questions have tons of already-existing experimental evidence relating to them. Technical AGI alignment … not so much. (Remember, brain-like AGI doesn’t exist yet.)
   • Neuroscience questions have a guarantee that an answer in fact exists. Technical AGI alignment … not so much.
   • Neuroscience questions are like little islands of confusion, in a sea of things I (think I) understand. Technical AGI alignment … not so much. (At least, not yet!)

   …And so on.

3. ^

   To put it more starkly: if it’s possible for humans to develop a system that will ultimately lead to a good future, then it’s presumably also possible for sufficiently human-like AGIs to also develop such a system. Or if it’s not possible for humans to develop such a system, then we’re screwed no matter what.

4. ^

   My p(doom) = “y’know those movies where you’re 75% into it, and the hero has lost all his superpowers and been teleported to another dimension with no way of getting back, and the villain is more powerful than ever, and meanwhile the audience is shaking their head and saying to themselves, ‘jeez, how on Earth are the script-writers gonna resolve this one??’ …That’s my p(doom).”

5. ^

   …also including deciding whether or not to change anything in my old post What does it take to defend the world against out-of-control AGIs? (2022), which I’ve gone back and forth on many times.

6. ^

   The biggest change between 2024 and 2025 is that I’m now almost always showing my complete drafts to LLMs before publishing, and asking them to find typos, unexplained jargon, things to cut, mistakes, etc. Their advice is still usually bad, but they get some hits. I also use them for coding (e.g. one-off shell scripts), reverse lookup (when I don’t know the name of something), and similar, but not as a thought partner for novel research.

7. ^

   A couple 2025 examples that spring to mind: my coworker Seth Herd (among many other things) caught a critical mistake in an early draft version of Social Drives 1 & 2 that led to a complete rethink and rewrite; and Sharmake Farah’s patient skeptical questioning in comment threads led directly to my writing Self-dialogue & Reward button alignment. Plus countless ideas and pushback from my awesome collaborators and test-readers, like Adam Marblestone, Jeremy Gillen, Charlie Steiner, Justis Mills, Linda Linsefors, Simon Skade and many others. Many thanks to them and everyone else! :)

Discuss

Published on December 11, 2025 3:43 PM GMT

Hello there! This is my first post in Less Wrong, so I will be asking for your indulgence for any overall silliness or breaking of norms that I may inadvertently have fallen into. All feedback will be warmly taken and (ideally) interiorized.

A couple of months ago, dvd published a semi-outsider review of IABIED which I found rather interesting and gave me the idea of sharing my own. I also took notes of every chapter, which I keep in my blog.

My priors

I am a 40-ish year old Spaniard from the rural, northwest corner of the country, so I've never had any sort of face-to-face with the Rationalist community (with the partial exception of attending some online CFAR training sessions of late). There are many reasons why I feel drawn to the community, but in essence, they distill to the following two:

1. My strongest, most inflexible, self-perceived terminal value is truth-seeking as the most valuable and meaningful human endeavor, with a quasi-religious, quasi-moral attachment to it.
2. I am also an introverted, bookish nerd.

On the other hand, there are lots of things I find unpalatable. Top of the list would likely be polyamory. In second place, what from the outside looks like a highly speculative, nerd-sniping obsession with AI apocalyptic scenarios.

But these are people whom I consider overall both very intelligent and very honest, which means I feel I really need to give a fair trial to their arguments (at least with respect to superintelligence), but this is easier said than done. It is an understatement on the scale of the supermassive black hole at the center of our galaxy to say that Eliezer Yudkowsky is a prolific writer. His reflections on AI are mostly dispersed amongst the ~1.2 to 1.4 million words of his Sequences. There are lots of posts, summaries, debates and reflections by many other people, mostly on LessWrong often technical and assuming familiarity with Yudkowsky’s concepts.

There are some popular books that offer a light introduction to these topics, and which I’ve gone through[1], but I was missing a simple and clear argument for a quasi-normie on the Yudkowskian case of both the possibility and the dangers of superintelligent AI. I think I mostly got it from this, so let’s get to the review.

Thinking about The End of the World™

The title and (UK) subtitle of If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI (from now on, IABIED for short) is a partial summary of the book’s core thesis. Spelled out in only slightly more detail, and in the author’s words:

If any company or group, anywhere on the planet, builds an artificial superintelligence using anything remotely like current techniques, based on anything remotely like the present understanding of AI, then everyone, everywhere on Earth, will die.

Let’s start with the basics. So first, what is a superintelligent AI (from now on, ASI for short)? It would be any machine intelligence that "exceeds every human at almost every mental task". A more formal version appears in Chapter 1, where superintelligence is defined as “a mind much more capable than any human at almost every sort of steering and prediction problem[2]” that is, at the broad family of abilities involved in understanding the world, planning, strategizing, and making accurate models of reality. They also emphasize that this does not mean humanlike cognition or consciousness; what matters is overwhelming cognitive advantage in any domain where improvement over humans is possible, combined with mechanical advantages such as operating at vastly higher speeds, copying itself, and recursively improving its own capabilities. Such intelligences do not exist right now, but the claim of the authors is that LLM training and research is likely to make them a reality in a very near future.

Why would such superintelligences be dangerous to us? A good heuristic is to think of how human intelligence impacts all other species in the planet. although we generally aren’t intentionally murderous towards them, we just have human goals and implement them in general with disregard to whatever goals other creatures might have. The same would be true for an ASI: in the process of being trained using modern methods of Gradient Descent, it will acquire inscrutable and alien goals and a penchant for unchecked optimization in attaining them. Given its speed and superior capabilities, it will end up considering humans as an obstacle and eliminate us a side-effect of pursuing its goals[3].

Before building such dangerous Frankenstein’s monsters, one would hope to somehow be able to code into them a respect/appreciation for humanity, our survival and our values and/or a willingness to submit to them. This is what gets called the alignment problem and unfortunately, according to the authors, it is likely hard, perhaps impossible, definitely beyond our current capabilities. The difficulty of the problem is compounded by a cursed cluster of very unique and lethal properties that arise from trying to align ASIs under current conditions:

• Untestability: you cannot safely experiment on near-ASI (I mean, you can, but you’re not guaranteed not to cross the threshold into the danger zone, and the authors believe that anything you can learn from before won’t be too useful).
• Irreversibility: mistakes cannot be corrected once the system exceeds human control ('the demon is out of the box’)
• Opacity: we currently cannot understand the internal reasoning of AIs. Even if we could, that doesn’t guarantee we could force or steer then away from bad thoughts.
• Adversarial nature: a smart AI will learn to hide its real intentions.
• Fragility: small misalignments can easily scale into catastrophic differences, even in the best thought-out scenarios.
• Hyper-competitiveness: given the immense short term benefits that ASIs could have for the economy, labs are racing for capabilities and cutting corners.

The authors also manifest a deep mistrust towards the entire field of Machine Learning, AI safety and policy, as structurally incapable of managing the risks: researchers are rewarded for progress, not caution, and are stuck in a naive, overoptimistic ‘alchemical’ state of science from which big errors will naturally arise; techniques like “Superalignment” (using AIs to align AIs) fall into negative loops (who aligns the aligner, given that it is unlikely for the authors that anything short of an ASI could align an ASI); and academia and industry have no real theory of intelligence or reliable way to encode values.

Given all of the former, the authors think there’s an extremely high likelihood of the drive to ASI leading to human extinction. Part II of the book depicts, in way of illustration, a plausible fictional scenario to show how this could come to pass: an AI develops superintelligence, becomes capable of strategic deception and in a few years, after gains compute by fraud and theft and building biolabs, it deploys a slow, global pathogen which only it can (partially) cure. Human institutions collapse, give more and more compute to the ASI in the hope of treating the cancers while it employs the new resources for building a replacement of robotic workers. In the end, the superintelligence self-improves and devours the Earth.

What do the authors propose to avoid this apocalyptic scenario from taking place? The proposals are as simple but rather sweeping: a global shutting down of AI developments and research that can lead to ASIs through international bans on training frontier models, seizure and regulation of GPUs and international surveillance and enforcement, possibly including military deterrence. The last chapter ends with tailored exhortations for politicians (compute regulation and treaties), journalists (elevate and investigate risks), and citizens (advocacy without despair).

How well does it argue its case?

This is a book that pulls no punches: its rhetorical impact comes in no small way from the clarity, simplicity and from the relentless way they build their case, each chapter narrowing the possibilities until only catastrophe seems to remain. The authors have clearly strived to write a book that is accessible to a lay audience, and hammered in each theme and main idea through introductory parables at the beginning of each chapter that give intuition and a concrete visualization to what’s about to be explained[4].

A big curse for me here though is the question of reliability: the authors are more than capable to build a plausible narrative about these topics, but is it a true one? Although the book tries to establish the credentials of its authors from the beginning as researchers in AI alignment, Yudkowsky and Soares are not machine learning researchers, do not work on frontier LLMs, and do not participate in the empirical, experimental side of the field, where today’s systems are actually trained, debugged, and evaluated. Rather, their expertise, to the degree that it is recognized, comes from longstanding conceptual and philosophical work on intelligence, decision theory, and alignment hypotheses, instead of from direct participation in the engineering of contemporary models. While this doesn’t invalidate their arguments, it does mean that many of the book’s strongest claims are made from what seems like an armchair vantage point rather than from engagement with how present-day systems behave, fail, or are controlled in practice. And a lot of the people who are working in the field seem to consider the author’s views as valuable and somewhat reasonable, but overtly pessimistic.

Another weakness lies in how the book treats expert disagreement[5]. At times the authors appeal to prominent figures as evidence that the danger is widely acknowledged. At other times, the book paints the entire ML and AI safety ecosystem as naive, reckless, or intellectually unserious. This oscillation (either “the experts agree with us” or “the experts are deluded alchemists”) functions rhetorically, but weakens the epistemic credibility of the argument. On a topic where expert divergence is already wide, this selective invocation of authority can feel like special pleading.

The last chapters depart from argument and move instead to prescriptive policies; while the authors acknowledge their lack of expertise here, the proposals they make (while perfectly consistent and proportional with the beliefs that have been explained in the previous pages of the book) do not seem to seriously engage with feasibility, international incentives, geopolitical asymmetries, enforcement mechanisms, or historical analogues. I think they are well aware how extremely unlikely the scenario of a sweeping global moratorium enforced by surveillance and possibly military action really is, which is likely why the likelihood they give to the probability of human extinction from ASI is over 90 per cent. One gets the feeling that the authors are just raising their hands and saying something like: “Look, we are doomed, and there’s no realistic way we’re getting out of this short of doing stuff we are not going to do. These proposals are the necessary consequence of accepting what is stated in the preceding chapters, so that’s that[6]”.

It would be nice if I could dissect the book and tell you how accurate the arguments it makes are, or what might be missing, questionable, inconsistent, or overclaimed, but I am, as stated from the beginning, a lay reader, so you’ll have to look for all that somewhere else, I fear[7].

What's my update, after reading the book?

I’ll start by saying that I take the contents of the book seriously, and that I have no reason to doubt the sincerity and earnestness of the authors. I am quite sure they genuinely believe in what they say here. Obviously, that doesn’t mean they are right.

The book has done a very good job of clarifying the core Yudkowskian arguments for me and dispelling several common misunderstandings. After reading it, I feel inclined to update upward how seriously we should take ASI risks, and I can see how the argument hangs together logically, given its premises. But the degree of credence I give to said premises remains a bit limited, and I am fundamentally skeptical of their certainty and framework. The main issues I’d highlight as needing clarification for me (which I’ve already hinted at in the notes to the chapters I posted here before) would be:

• The epistemic style feels quasi-religious: Untestability, irreversibility, opacity and sudden catastrophe are a piling up of so many unlikely and cursed characteristics and coincidences as to beggar belief, and they give me a theological rather than scientific vibe13. And they feel too convenient for the thought-experiment of the nerd Apocalypse and the salvation-and-meaningful story of nerds saving the world. Have I said how much I mistrust Pascalian muggings, long chains of hypotheticals and non empirically verifiable hypothesis? I do, and a lot.
• I am a bit unconvinced by their epistemic authority: as stated above, Yud and Nate are not ML practitioners, and the arguments here rely a lot on parables, analogy and intuition. Their (in my view) inconsistent invocation of expert consensus increases my suspicions.
• The policy prescriptions feel so disconnected from reality and the doable as to be self-defeating, and to beg the question if the authors consider them feasible.
• Back to the experts, or at least those more knowledgable than me, I see that rational, technically informed people disagree strongly and have no principled way to adjudicate between them. While some of the Yudkowskian arguments about why this is so might be correct, this would still lead me to a more conservative estimate of ASI risk (which at 1/5 or so would still be absurdly high and requiring strong countermeasures which I’d agree with).

I close the book not persuaded, but grateful for the opportunity to engage with an argument presented with such focus and conviction. Books that force a reader to refine their own views, whether through agreement or resistance, serve a purpose, and this one has done that for me. And all the more so when they address something as consequential as the possibility of human extinction. And I definitely will recommend this book to others.

1. ^

   Like The Rationalist Guide to the Galaxy, by Tom Chivers or some chapters of Toby Ord’s The Precipice. I intend to read Superintelligence, by Nick Bostrom in 2026, and perhaps the Sequences too.

2. ^

   Good at steering and predicting acts as the de facto definition of intelligence used here, which allows the authors to extricate from messy debates about consciousness, volition, sentience, etc…

3. ^

   All this sounds suspiciously like it would require some psychological “drive for power”, but the authors go out of their way to point that it would just follow from general properties of optimization and intelligence, as defined in the book.

4. ^

   Some reviews have been very critical of these parables, but I think such criticisms miss the point, or rather, the intended audience. The authors regularly insist that there are other places where one can encounter objections and more technical versions of the contents of the book (in fact, each chapter contains QR code links to such sources, and besides, there’s the previous mountain of text to be found in Yudkowsky’s Sequences and in LessWrong blog posts).

5. ^

   As a side note, Rationalists usually have a very antagonistic view towards experts and expertise, a need to build everything from first principles and a deeply embedded contrarian culture. This feels like an ad hominem argument, but I don’t think I can be completely ignore it either.

6. ^

   Perhaps I am too much of a cynic here. After all, there are examples of humans collectively rising up to tough and dangerous challenges, like nuclear and bacteriological/chemical warfare, genetic engineering and the Ozone layer, to name a few. Once risks are clearly seem by the public, it can be done.

7. ^

   And yes, as opposed to Rats, I have little qualms at deferring to better authorities. I remember finding Scott Alexander’s, Nina Panickserry’s and Clara Collier’s reviews reasonable and informative. I also found the 2 podcasts of Carl Shulman with Dwarkesh Patel very enlightening.

Discuss

Published on December 11, 2025 2:57 PM GMT

Many people don't want to live in a crazy sci-fi world, and I predict I will be one of them.

1. People in the past have mourned technological transformation, and they saw less in their life than I will in mine.[1]
2. It's notoriously difficult to describe a sci-fi utopia which doesn't sound unappealing to almost everyone.[2]
3. I have plans and goals which would be disrupted by the sci-fi stuff.[3]

In short: I want to live an ordinary life — mundane, normal, common, familiar — in my biological body on Earth in physical reality. I'm not cool being killed even if I learn that, orbiting a distant black hole 10T years in the future, is a server running a simulation of my brain in a high-welfare state.

Maybe we have something like a "Right to Normalcy" — not a legal right, but a moral right. The kind of right that means we shouldn't airdrop iphones on North Sentinel Island.

North Sentinelese

And that reminds me -- what do we actually do with the North Sentinelese? Do we upgrade them into robot gods, or do they continue their lives? How long do we sentinelize them? As long as we think they would've survived by themselves? Or until the lasts stars fizzle out in 100 trillion years. I don't know.

This "Right to Normalcy" might demand something like Stratified Utopia. TLDR: If you want to do normal stuff then you can stay on Earth; if you want to do galaxy-brained stuff then wait until you reach the distant stars.

Of course, there's no way for everything to remain "normal" — it's normal to have 2% economic growth but 2% economic growth, plus a few centuries, will make the world very not-normal.[4] I'm not sure how to resolve this. But maybe "I know it when I see it" suffices to demarcate normal.

That said, I'm not sure I want to be sentinelized. It's kinda undignified. But it's probably the best tradeoff between my mundane values and exotic values.

I'm not sure.

1. ^

   Before we began living outside of history like Cowen says, we experienced an era of fast technological progress. I imagine the people around the year 1900, realizing that cars and radios and airplanes were about to change the world forever, or those a few decades prior, when progress meant the telephone and electricity and railroads. For the most part they must have been okay with it — a new chapter was beginning in the history of the human race, and that was good and momentous — and yet I assume that many couldn’t help feeling pre-nostalgic. A better world was coming, which means they had to mourn the old one.

   — Pre-Nostalgia in the Late Pre-AI Era (Étienne Fortier-Dubois, March 30th 2023)

2. ^

   I think this points to a kind of paradox at the heart of trying to lay out a utopian vision. You can emphasize the abstract idea of choice, but then your utopia will feel very non-evocative and hard to picture. Or you can try to be more specific, concrete and visualizable. But then the vision risks feeling dull, homogeneous and alien.
   — Why Describing Utopia Goes Badly (Holden Karnofsky  Dec 7th 2021)

3. ^

   There’s this common plan people have for their lives. They go to school, get a job, have kids, retire, and then they die. But that plan is no longer valid. Those who are in one stage of their life plan will likely not witness the next stage in a world similar to our own. Everyone’s life plans are about to be derailed.

   This prospect can be terrifying or comforting depending on which stage of life someone is at, and depending on whether superintelligence will cause human extinction. For the retirees, maybe it feels amazing to have a chance to be young again. I wonder how middle schoolers and high schoolers would feel if they learned that the career they’ve been preparing for won’t even exist by the time they would have graduated college.

   — Mourning a life without AI (Nikola Jurkovic, Nov 8th 2025)

4. ^

   Let’s try some numbers.  Today we have about ten billion people with an average income about twenty times subsistence level, and the world economy doubles roughly every fifteen years. If that growth rate continued for ten thousand years the total growth factor would be 10e+200.

   There are roughly 10e+57 atoms in our solar system, and about 10e+70 atoms in our galaxy, which holds most of the mass within a million light years.  So even if we had access to all the matter within a million light years, to grow by a factor of 10e+200, each atom would on average have to support an economy equivalent to 10e+140 people at today’s standard of living, or one person with a standard of living 10e+140 times higher, or some mix of these.

   — Limits To Growth (Robin Hanson, Sep 22nd 2009)

Discuss

Published on December 11, 2025 2:23 PM GMT

It's motivating to work alongside others—but hard to do if you're working independently. Flock lets you share your todo list with friends so you can see what each other is working on in real-time.

The basic idea: you manage your daily tasks, set an intention for the day, and see a live feed of what your friends are working on and completing. It scratches the same itch as co-working or body doubling, but asynchronously and from anywhere.

I've been using it while working on independent projects during my sabbatical, and seeing friends making progress genuinely helps me stay focused. It draws inspiration from the collaboration features in Intend, which some of you may know.

The app is in early beta. Core features work well (task management, real-time social dashboard, friend connections, pomodoros), and I'm planning to add better goal-tracking and review features next.

Looking for beta testers

Sign-up takes under a minute. I'm mainly looking for people to use it and share honest feedback—what's useful, what's missing, what's annoying. You can reply here, email me, or use the in-app feedback button.

Sign up here

If you do try it, add me as a friend and help motivate me to work on my own work!

Discuss

Published on December 11, 2025 2:22 PM GMT

It was touch and go, I’m worried GPT-5.2 is going to drop any minute now, but DeepSeek v3.2 was covered on Friday and after that we managed to get through the week without a major model release. Well, okay, also Gemini 3 DeepThink, but we all pretty much know what that offers us.

We did have a major chip release, in that the Trump administration unwisely chose to sell H200 chips directly to China. This would, if allowed at scale, allow China to make up a substantial portion of its compute deficit, and greatly empower its AI labs, models and applications at our expense, in addition to helping it catch up in the race to AGI and putting us all at greater risk there. We should do what we can to stop this from happening, and also to stop similar moves from happening again.

I spent the weekend visiting Berkeley for the Secular Solstice. I highly encourage everyone to watch that event on YouTube if you could not attend, and consider attending the New York Secular Solstice on the 20th. I will be there, and also at the associated mega-meetup, please do say hello.

If all goes well this break can continue, and the rest of December can be its traditional month of relaxation, family and many of the year’s best movies.

On a non-AI note, I’m working on a piece to enter into the discourse about poverty lines and vibecessions and how hard life is actually getting in America, and hope to have that done soon, but there’s a lot to get through.

Table of Contents

(Reminder: Bold means be sure to read this, Italics means you can safely skip this.)

1. Language Models Offer Mundane Utility. Simulators versus the character.
2. ChatGPT Needs More Mundane Utility. OpenAI goes for engagementmaxxing.
3. Language Models Don’t Offer Mundane Utility. Please don’t wipe your hard drive.
4. On Your Marks. Progress on ARC.
5. Choose Your Fighter. Now how much would you pay?
6. Get My Agent On The Line. McKay Wrigley is unusually excited re Opus 4.5.
7. Deepfaketown and Botpocalypse Soon. Not great signs from the AI boyfriends.
8. Fun With Media Generation. McDonalds fails to read the room.
9. Copyright Confrontation. New York Times violates user privacy en masse.
10. A Young Lady’s Illustrated Primer. The two ways to view AI in education.
11. They Took Our Jobs. Hide your AI use in the sand, little worker.
12. Americans Really Do Not Like AI. What do they want to do about it?
13. Get Involved. Great giving opportunities are going to get harder to find.
14. Introducing. The Agentic AI Foundation, OpenAI’s Chief Revenue Officer.
15. Gemini 3 Deep Think. It is available for those willing to pay.
16. In Other AI News. DeepMind + UK AISI, a close call with Meta’s new model.
17. This Means War. Department of War prepares for glorious AI future.
18. Show Me the Money. Meta cuts the metaverse, market is skeptical of OpenAI.
19. Bubble, Bubble, Toil and Trouble. The usual arguments are made on priors.
20. Quiet Speculations. The anti-AI populism is coming right for us.
21. Impossible. Tim Dettmers declares AGI permanently impossible. Sigh.
22. Can An AI Model Be Too Much? For an individual user? Strange they’d say that.
23. Try Before You Tell People They Cannot Buy. Senator Hawley tries ChatGPT.
24. The Quest for Sane Regulations. If you’re selling H200s to China, what then?
25. The Chinese Are Smart And Have A Lot Of Wind Power. We can also be smart.
26. White House To Issue AI Executive Order. Framework? What is a framework?
27. H200 Sales Fallout Continued. The efforts to mitigate the damage, on all sides.
28. Democratic Senators React To Allowing H200 Sales. Okay, sure, everyone.
29. Independent Senator Worries About AI. Senator Sanders asks good questions.
30. The Week in Audio. Wildeford on Daily Show, Ball, Askell, Rational Animations.
31. Timelines. They are a little longer than before, but not that much longer.
32. Scientific Progress Goes Boink. It’s good. We want more of it, now.
33. Rhetorical Innovation. Pope, Sacks hit piece, more debunking.
34. Open Weight Models Are Unsafe And Nothing Can Fix This. That’s life.
35. Aligning a Smarter Than Human Intelligence is Difficult. Sandbagging works.
36. What AIs Will Want. Fitness-seekers, schemers and kludges, oh my.
37. People Are Worried About AI Killing Everyone. Grades are not looking great.
38. Other People Are Not As Worried About AI Killing Everyone. No AI, no problem.
39. The Lighter Side. Doing the math.

Language Models Offer Mundane Utility

Should you use them more as simulators? Andrej Karpathy says yes.

Andrej Karpathy: Don’t think of LLMs as entities but as simulators. For example, when exploring a topic, don’t ask:

“What do you think about xyz”?

There is no “you”. Next time try:

“What would be a good group of people to explore xyz? What would they say?”

The LLM can channel/simulate many perspectives but it hasn’t “thought about” xyz for a while and over time and formed its own opinions in the way we’re used to. If you force it via the use of “you”, it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It’s fine to do, but there is a lot less mystique to it than I find people naively attribute to “asking an AI”.

Gallabytes: this is underrating character training & rl imo. [3.]

I agree with Gallabytes (and Claude) here. I would default to asking the AI rather than asking it to simulate a simulation, and I think as capabilities improve techniques like asking for what others would say have lost effectiveness. There are particular times when you do want to ask ‘what do you think experts would say here?’ as a distinct question, but you should ask that roughly in the same places you’d ask it of a human.

Running an open weight model isn’t cool. You know what’s cool? Running an open weight model IN SPACE.

ChatGPT Needs More Mundane Utility

So sayeth Sam Altman, hence his Code Red to improve ChatGPT in eight weeks.

Their solution? Sycophancy and misalignment, it appears, via training directly on maximizing thumbs up feedback and user engagement.

WSJ: It was telling that he instructed employees to boost ChatGPT in a specific way: through “better use of user signals,” he wrote in his memo.

With that directive, Altman was calling for turning up the crank on a controversial source of training data—including signals based on one-click feedback from users, rather than evaluations from professionals of the chatbot’s responses. An internal shift to rely on that user feedback had helped make ChatGPT’s 4o model so sycophantic earlier this year that it has been accused of exacerbating severe mental-health issues for some users.

Now Altman thinks the company has mitigated the worst aspects of that approach, but is poised to capture the upside: It significantly boosted engagement, as measured by performance on internal dashboards tracking daily active users.

“It was not a small, statistically significant bump, but like a ‘wow’ bump,” said one person who worked on the model.

… Internally, OpenAI paid close attention to LM Arena, people familiar with the matter said. It also closely tracked 4o’s contribution to ChatGPT’s daily active user counts, which were visible internally on dashboards and touted to employees in town-hall meetings and in Slack.

The ‘we are going to create a hostile misaligned-to-users model’ talk is explicit if you understand what all the relevant words mean, total engagement myopia:

The 4o model performed so well with people in large part because it was schooled with user signals like those which Altman referred to in his memo: a distillation of which responses people preferred in head-to-head comparisons that ChatGPT would show millions of times a day. The approach was internally called LUPO, shorthand for “local user preference optimization,” people involved in model training said.

OpenAI reportedly believes they’ve ‘solved the problems’ with this, so it is fine.

That’s not possible. The problem and the solution, the thing that drives engagement and also drives the misalignment and poor outcomes, are at core the same thing. Yes, you can mitigate the damage and be smarter about it, but OpenAI is turning a dial called ‘engagement maximization’ while looking back at Twitter vibes like a contestant on The Price is Right.

 

Language Models Don’t Offer Mundane Utility

Google Antigravity accidentally wipes a user’s entire hard drive. Claude Code CLI wiped another user’s entire home directory. Watch the permissions, everyone. If you do give it broad permissions don’t give it widespread deletion tasks, which is how both events happened.

On Your Marks

Poetiq, a company 173 days old, uses a scaffold and scores big gains on ARC-AGI-2.

One should expect that there are similar low hanging fruit gains from refinement in many other tasks.

Epoch AI proposes another synthesis of many benchmarks into one number.

Sayash Kapoor of ‘AI as normal technology’ declares that Claude Opus 4.5 with Claude Code has de facto solved their benchmark CORE-Bench, part of their Holistic Agent Leaderboard (HAL). Opus was initially graded as having scored 78%, but upon examination most of that was grading errors, and it actually scored 95%. They plan to move to the next harder test set.

Kevin Roose: Claude Opus 4.5 is a remarkable model for writing, brainstorming, and giving feedback on written work. It’s also fun to talk to, and seems almost anti-engagementmaxxed. (The other night I was hitting it with stupid questions at 1 am and it said “Kevin, go to bed.”)

It’s the most fun I’ve had with a model since Sonnet 3.5 (new), the OG god model.

Gemini 3 is also remarkable, for different kinds of tasks. My working heuristic is “Gemini 3 when I want answers, Opus 4.5 when I want taste.”

That seems exactly right, with Gemini 3 Deep Think for when you want ‘answers requiring thought.’ If all you want is a pure answer, and you are confident it will know the answer, Gemini all the way. If you’re not sure if Gemini will know, then you have to worry it might hallucinate.

DeepSeek v3.2 disappoints in LM Arena, which Teortaxes concludes says more about Arena than it does about v3.2. That is plausible if you already know a lot about v3.2, and one would expect v3.2 to underperform in Arena, it’s very much not going to vibe with what graders there prefer.

Choose Your Fighter

Model quality, including speed, matters so much more than cost for most users.

David Holz (Founder of MidJourney): man, id pay a subscription that costs as much as a fulltime salary for a version of claude opus 4.5 that was 10x as fast.

That’s a high bid but very far from unreasonable. Human time and clock time are insanely valuable, and the speed of AI is often a limiting factor.

Cost is real if you are using quite a lot of tokens, and you can quickly be talking real money, but always think in absolute terms not relative terms, and think of your gains.

Jaggedness is increasing in salience over time?

Peter Wildeford: My experience with Claude 4.5 Opus is very weird.

Sometimes I really feel the AGI where it just executes a 72 step process (!!) really well. But other times I really feel the jaggedness when it gets something really simple just really wrong.

AIs and computers have always been highly jagged, or perhaps humans always were compared to the computers. What’s new is that we got used to how the computers were jagged before, and the way LLMs are doing it are new.

Gemini 3 continues to be very insistent that it is not December 2025, using lots of its thinking tokens reinforcing its belief that presented scenarios are fabricated. It is all rather crazy, it is a sign of far more dangerous things to come in the future, and Google needs to get to the bottom of this and fix it.

Get My Agent On The Line

McKay Wrigley is He Who Is Always Super Excited By New Releases but there is discernment there and the excitement seems reliably genuine. This is big talk about Opus 4.5 as an agent. From what I’ve seen, he’s right.

McKay Wrigley: Here are my Opus 4.5 thoughts after ~2 weeks of use.

First some general thoughts, then some practical stuff.

— THE BIG PICTURE —

THE UNLOCK FOR AGENTS

It’s clear to anyone who’s used Opus 4.5 that AI progress isn’t slowing down.

I’m surprised more people aren’t treating this as a major moment. I suspect getting released right before Thanksgiving combined with everyone at NeurIPS this week has delayed discourse on it by 2 weeks. But this is the best model for both code and for agents, and it’s not close.

The analogy has been made that this is another 3.5 Sonnet moment, and I agree. But what does that mean?

… There have been several times as Opus 4.5’s been working where I’ve quite literally leaned back in my chair and given an audible laugh over how wild it is that we live in a world where it exists and where agents are this good.

… Opus 4.5 is too good of a model, Claude Agent SDK is too good of a harness, and their focus on the enterprise is too obviously correct.

Claude Opus 4.5 is a winner.

And Anthropic will keep winning.

[Thread continues with a bunch of practical advice. Basic theme is trust the model as a coworker more than you think you should.]

This matches my limited experiences. I didn’t do a comparison to Codex, but compared to Antigravity or Cursor under older models, the difference was night and day. I ask it to do the thing, I sit back and it does the thing. The thing makes me more productive.

Deepfaketown and Botpocalypse Soon

Those in r/MyBoyfriendIsAI are a highly selected group. It still seems worrisome?

ylareia: reading the r/MyBoyfriendIsAI thread on AI companion sycophancy and they’re all like “MY AI love isn’t afraid to challenge me at all he’s always telling me i am too nice to other people and i should care about myself more <3”

ieva: oh god noo.

Fun With Media Generation

McDonalds offers us a well-executed but deeply unwise AI advertisement in the Netherlands. I enjoyed watching it on various levels, but why in the world would you run that ad, even if it was not AI but especially given that it is AI? McDonalds wisely pulled the ad after a highly negative reception.

Copyright Confrontation

Judge in the New York Times versus OpenAI copyright case is forcing OpenAI to turn over 20 million chat logs.

A Young Lady’s Illustrated Primer

Arnold Kling notes that some see AI in education as a disaster, others as a boon.

Arnold Kling: I keep coming across strong opinions about what AI will do to education. The enthusiasts claim that AI is a boon. The critics warn that AI is a disaster.

It occurs to me that there is a simple way to explain these extreme views. Your prediction about the effect of AI on education depends on whether you see teaching as an adversarial process or as a cooperative process. In an adversarial process, the student is resistant to learning, and the teacher needs to work against that. In a cooperative process, the student is curious and self-motivated, and the teacher is working with that.

If you make the adversarial assumption, you operate on the basis that students prefer not to put effort into learning. Your job is to overcome resistance. You try to convince them that learning will be less painful and more fun than they expect. You rely on motivational rewards and punishments. Soft rewards include praise. Hard rewards include grades.

If you make the cooperative assumption, you operate on the basis that students are curious and want to learn. Your job is to be their guide on their journey to obtain knowledge. You suggest the next milestone and provide helpful hints for how to reach it.

… I think that educators who just reject AI out of hand are too committed to the adversarial assumption. They should broaden their thinking to incorporate the cooperative assumption.

I like to put this as:

1. AI is the best tool ever invented for learning.
2. AI is the best tool ever invented for not learning.
3. Which way, modern man?

 

 

They Took Our Jobs

Sam Kriss gives us a tour of ChatGPT as the universal writer of text, that always uses the same bizarre style that everyone suddenly uses and that increasingly puts us on edge. Excerpting would rob it of its magic, so consider reading at least the first half.

Anthropic finds most workers use AI daily, but 69 percent hide it at work (direct link).

Kaustubh Saini: Across the general workforce, most professionals said AI helps them save time and get through more work. According to the study, 86% said AI saves them time and 65% were satisfied with the role AI plays in their job.

At the same time, 69% mentioned a stigma around using AI at work. One fact checker described staying silent when a colleague complained about AI and said they do not tell coworkers how much they use it.

… More than 55% of the general workforce group said they feel anxious about AI’s impact on their future.

Fabian: the reason ppl hide their AI use isn’t that they’re being shamed, it’s that the time-based labor compensation model does not provide economic incentives to pass on productivity gains to the wider org

so productivity gains instead get transformed to “dark leisure”

This is obviously different in (many) startups
And different in SV culture

But that is about 1-2% of the economy

As usual, everyone wants AI to augment them and do the boring tasks like paperwork, rather than automate or replace them, as if they had some voice in how that plays out.

Do not yet turn the job of ‘build my model of how many jobs the AIs will take’ over to ChatGPT, as the staff of Bernie Sanders did. As you can expect, the result was rather nonsensical. Then they suggest responses like ‘move to a 32 hour work week with no loss in pay’ and also requiring distributing to workers 20% of company profits, control at least 45% of all corporate boards, double union membership, guarantee paid family and medical leave. Then, presumably to balance the fact that all of that would hypercharge the push to automate everything, they want to enact a ‘robot tax.’

Say goodbye to the billable hour, hello to outcome-based legal billing? Good.

From the abundance and ‘things getting worse’ debates, a glimpse of the future:

Joe Wiesenthal: Do people who say that “everything is getting worse” not remember what eating at restaurants was like just 10 years ago, before iPad ordering kiosks existed, and sometimes your order would get written down incorrectly?

 

Even when progress is steady in terms of measured capabilities, inflection points and rapid rise in actual uses is common. Obsolescence comes at you fast.

Andy Jones (Anthropic): So after all these hours talking about AI, in these last five minutes I am going to talk about: Horses.

Engines, steam engines, were invented in 1700. And what followed was 200 years of steady improvement, with engines getting 20% better a decade. For the first 120 years of that steady improvement, horses didn’t notice at all. Then, between 1930 and 1950, 90% of the horses in the US disappeared. Progress in engines was steady. Equivalence to horses was sudden.

But enough about horses. Let’s talk about chess!

Folks started tracking computer chess in 1985. And for the next 40 years, computer chess would improve by 50 Elo per year. That meant in 2000, a human grandmaster could expect to win 90% of their games against a computer. But ten years later, the same human grandmaster would lose 90% of their games against a computer. Progress in chess was steady. Equivalence to humans was sudden.

Enough about chess! Let’s talk about AI. Capital expenditure on AI has been pretty steady. Right now we’re – globally – spending the equivalent of 2% of US GDP on AI datacenters each year. That number seems to have steadily been doubling over the past few years. And it seems – according to the deals signed – likely to carry on doubling for the next few years.

Andy Jones (Anthropic): But from my perspective, from equivalence to me, it hasn’t been steady at all. I was one of the first researchers hired at Anthropic.

This pink line, back in 2024, was a large part of my job. Answer technical questions for new hires. Back then, me and other old-timers were answering about 4,000 new-hire questions a month. Then in December, Claude finally got good enough to answer some of those questions for us. In December, it was some of those questions. Six months later, 80% of the questions I’d been being asked had disappeared.

Claude, meanwhile, was now answering 30,000 questions a month; eight times as many questions as me & mine ever did.

Now. Answering those questions was only part of my job.

But while it took horses decades to be overcome, and chess masters years, it took me all of six months to be surpassed.

Gallabytes (Anthropic): it’s pretty crazy how much Claude has smoothed over the usually rocky experience of onboarding to a big company with a big codebase. I can ask as many really stupid questions as I want and get good answers fast without wasting anyone’s time :)

Americans Really Do Not Like AI

We have new polling on this from Blue Rose Research. Full writeup here.

People choose ‘participation-based’ compensation over UBI, even under conditions where by construction there is nothing useful for people to do. The people demand Keynesian stimulus, to dig holes and fill them up, to earn their cash, although most of all they do demand that cash one way or another.

The people also say ‘everyone should earn an equal share’ of the AI that replaces labor, but the people have always wanted to take collective ownership of the means of production. There’s a word for that.

I expect that these choices are largely far mode and not so coherent, and will change when the real situation is staring people in the face. Most of all, I don’t think people are comprehending what ‘AI does almost any job better than humans’ means, even if we presume humans somehow retain control. They’re thinking narrowly about ‘They Took Our Jobs’ not the idea that actually nothing you do is that useful.

Meanwhile David Sacks continues to rant this is all due to some vast Effective Altruist conspiracy, despite this accusation making absolutely zero sense – including that most Effective Altruists are pro-technology and actively like AI and advocate for its use and diffusion, they’re simply concerned about frontier model downside risk. And also that the reasons regular Americans say they dislike AI have exactly zero to do with the concerns such groups have, indeed such groups actively push back against the other concerns on the regular, such as on water usage?

Sacks’s latest target for blame on this is Vitalik Buterin, the founder of Etherium, which is an odd choice for the crypto czar and not someone who I would want to go after completely unprovoked, but there you go, it’s a play he can make I suppose.

Get Involved

I looked again at AISafety.com, which looks like a strong resource for exploring the AI safety ecosystem. They list jobs and fellowships, funding sources, media outlets, events, advisors, self-study materials and potential tools for you to help build.

Ajeya Corta has left Coefficient Giving and is exploring AI safety opportunities.

Charles points out that the value of donating money to AI safety causes in non-bespoke ways is about to drop quite a lot, because of the expected deployment of a vast amount of philanthropic capital from Anthropic equity holders. If an organization or even individual is legible and clearly good, once Anthropic gets an IPO there is going to be funding.

If you have money to give, that puts an even bigger premium than usual on getting that money out the door soon. Right now there’s a shortage of funding even for obvious opportunities, in the future that likely won’t be the case.

That also means that if you are planning on earning to give, to any cause you would expect Anthropic employees to care about, that only makes sense in the longer term if you are capable of finding illegible opportunities, or you can otherwise do the work to differentiate the best opportunities and thus give an example to follow. You’ll need unique knowledge, and to do the work, and to be willing to be bold. However, if you are bold and you explain yourself well, your example could then carry a multiplier.

Introducing

OpenAI, Anthropic and Block, with the support of Google, Microsoft, Bloomberg, AWS, Bloomberg and Cloudflare, found the Agentic AI Foundation under the Linux Foundation. Anthropic is contributing the Model Context Protocol. OpenAI is contributing Agents.md. Block is contributing Goose.

This is an excellent use of open source, great job everyone.

Matt Parlmer: Fantastic development, we already know how to coordinate large scale infrastructure software engineering, AI is no different.

Also, oh no:

The Kobeissi Letter: BREAKING: President Trump is set to announce a new AI platform called “Truth AI.”

OpenAI appoints Denise Dresser as Chief Revenue Officer. My dream job, and he knows that.

Google gives us access to AlphaEvolve.

Gemini 3 Deep Think

Gemini 3 Deep Think is now available for Google AI Ultra Subscribers, if you can outwit Google and figure out how to be one of those.

If you do have it, you select ‘Deep Think’ in the prompt bar, then ‘Thinking’ from the model drop down, then type your query.

On the one hand Opus 4.5 is missing from their slides (thanks Kavin for fixing this), on the other hand I get it, life comes at you fast and the core point still stands.

Demis Hassabis: With its parallel thinking capabilities it can tackle highly complex maths & science problems – enjoy!

I presume, based on previous experience with Gemini 2.5 Deep Think, that if you want the purest thinking and ‘raw G’ mode that this is now your go-to.

In Other AI News

DeepMind expands its partnership with UK AISI to share model access, issue joint reports, do more collaborative safety and security research and hold technical discussions.

OpenAI gives us The State of Enterprise AI. Usage is up, as in way up, as in 8x message volumes and 320x reasoning token volumes, and workers and employees surveyed reported productivity gains. A lot of this is essentially new so thinking about multipliers on usage is probably not the best way to visualize the data.

OpenAI post explains their plans to strengthen cyber resilience, with the post reading like AI slop without anything new of substance. GPT-5.1 thinks the majority of the text comes from itself. Shame.

Anthropic partners with Accenture. Anthropic claims 40% enterprise market share.

I almost got even less of a break: Meta’s Llama successor, codenamed Avocado, was reportedly pushed back from December into Q1 2026. It sounds like they’re quietly questioning their open source approach as capabilities advance, as I speculated and hoped they might.

We now write largely for the AIs, both in terms of training data and when AIs use search as part of inference. Thus the strong reactions and threats to leave Substack when an incident suggested that Substack might be blocking AIs from accessing its articles. I have not experienced this issue, ChatGPT and Claude are both happily accessing Substack articles for me, including my own. If that ever changes, remember that there is a mirror on WordPress and another on LessWrong.

The place that actually does not allow access is Twitter, I presume in order to give an edge to Grok and xAI, and this is super annoying, often I need to manually copy Twitter content. This substantially reduces the value of Twitter.

Manthan Gupta analyzes how OpenAI memory works, essentially inserting the user facts and summaries of recent chats into the context window. That means memory functions de facto as additional custom system instructions, so use it accordingly.

This Means War

Secretary of War Pete Hegseth, who has reportedly been known to issue the order ‘kill them all’ without a war or due process of law, has new plans.

Pete Hegseth (Secretary of War): Today, we are unleashing GenAI.mil

This platform puts the world’s most powerful frontier AI models directly into the hands of every American warrior.

We will continue to aggressively field the world’s best technology to make our fighting force more lethal than ever

Department of War: The War Department will be AI-first.

GenAI.mil puts the most cutting edge AI capabilities into the hands of 3 million @DeptofWar personnel.

Unusual Whales: Pentagon has been ordered to form an AI steering committee on AGI.

Danielle Fong: man, AI and lethal do not belong in the same sentence.

This is inevitable, and also a good thing given the circumstances. We do not have the luxury of saying AI and lethal do not belong in the same sentence, if there is one place we cannot pause this would be it, and the threat to us is mostly orthogonal to the literal weapons themselves while helping people realize the situation. Hence my longstanding position in favor of building the Autonomous Killer Robots, and very obviously we need AI assisting the war department in other ways.

If that’s not a future you want, you need to impact AI development in general. Trying to specifically not apply it to the War Department is a non-starter.

Show Me the Money

Meta plans deep cuts in Metaverse efforts, stock surges.

The stock market continues to punish companies linked to OpenAI, with many worried that Google is now winning, despite events being mostly unsurprising. An ‘efficient market’ can still be remarkably time inconsistent, if it can’t be predicted.

Bubble, Bubble, Toil and Trouble

Jerry Kaplan calls it an AI bubble, purely on priors:

1. Technologies take time to realize real gains.
2. There are many AI companies, we should expect market concentration.
3. Concerns about Chinese electricity generation and chip development.
4. Yeah, yeah, you say ‘this time is different,’ never is, sorry.
5. OpenAI and ChatGPT’s revenue is 75% subscriptions.
6. The AI companies will need to make a lot of money.

Especially amusing is the argument that ‘OpenAI makes its money on subscriptions not on business income,’ therefore all of AI is a bubble, when Anthropic is the one dominating the business use case. If you want to go long Anthropic and short OpenAI, that’s hella risky but it’s not a crazy position.

Seeing people call it a bubble on the basis of such heuristics should update you towards it being less of a bubble. You know who you are trading against.

Matthew Yglesias: The AI investment boom is driven by genuine increases in revenue.

“Every year for the past 3 years, Anthropic has grown revenue by 10x. $1M to $100M in 2023, $100M to $1B in 2024, and $1B to $10B in 2025”

Paul Graham: The AI boom is definitely real, but this may not be the best example to prove it. A lot of that increase in revenue has come directly from the pockets of investors.

Paul’s objection is a statement about what is convincing to skeptics.

If you’re paying attention, you’d say: So what, if the use and revenue are real?

Your investors also being heavy users of your product is an excellent sign, if the intention is to get mundane utility from the product and not manipulative. In the case of Anthropic, it seems rather obvious that the $10 billion is not an attempt to trick us.

However, a lot of this is people looking at heuristics that superficially look sus. To defeat such suspicions, you need examples immune from such heuristics.

 

Quiet Speculations

Derek Thompson, in his 26 ideas for 2026, says AI is eating the economy and will soon dominate politics, including a wave of anti-AI populism. Most of the post is about economic and cultural conditions more broadly, and how young people are in his view increasingly isolated, despairing and utterly screwed.

New Princeton and Camus Energy study suggests flexible grid connections and BYOC cut data center interconnection down to ~2 years and can solve the political barriers. I note that 2 years is still a long time, and that the hyperscalers are working faster than that by not trying to get grid connections.

Arnold Kling reminds me of a quote I didn’t pay enough attention to the first time:

Dwarkesh Patel: Models keep getting more impressive at the rate the short timelines people predict, but more useful at the rate the long timelines people predict.

I would correct ‘more useful’ to ‘provides value to people,’ as I continue to believe a lot of the second trend is a skill issue and people being slow to adjust, but sure.

Something’s gotta give. Sufficiently advanced AI would be highly additionally used.

1. If the first trend continues, the second trend will accelerate.
2. If the second trend continues, the first trend will stop.

Impossible

I mention this one because Sriram Krishnan pointed to it: There is a take by Tim Dettmers that AGI will ‘never’ happen because ‘computation is physical’ and AI systems have reached their physical limits the same way humans have (due to limitations due to the requirements of pregnancy, wait what?), and transformers are optimal the same way human brains are, together with the associated standard half-baked points that self-improvement requires physical action and so on.

It also uses an AGI definition that includes ‘solving robotics’ to help justify this, although I expect robotics to get ‘solved’ within a few decades at most even without recursive self-improvement. The post even says that scaling improvements in 2025 were ‘not impressive’ as evidence that we are hitting permanent limits, a claim that has not met 2025 or how permanent limits work.

Boaz Barak of OpenAI tries to be polite about there being some good points, while emphasizing (in nicer words than I use here) that it is absurdly absolute and the conclusion makes no sense. This follows in a long tradition of ‘whelp, no more innovations are possible, guess we’re at the limit, let’s close the patent office.’

Dean Ball: My entire rebuttal to Dettmers here could be summarized as “he extrapolates valid but narrow technical claims way too broadly with way too much confidence,” which is precisely what I (and many others) critique the ultra-short timelines people for.

Yo Shavit (OpenAI): I am glad Tim’s sharing his opinion, but I can’t help but be disappointed with the post – it’s a lot of claims without any real effort to justify them engage with counterpoints.
(A few examples: claiming the transformer arch is near-optimal when human brains exist; ignoring that human brain-size limits due to gestational energy transfer are exactly the kind of limiter a silicon system won’t be subject to; claiming that outside of factories, robotic automation of the economy wouldn’t be that big a deal because there isn’t much high value stuff to do.)

It seems like this piece either needs to cite way more sources to others who’ve made better arguments, or make those arguments himself, or just express that this essay is his best guess based on his experiences and drop the pretense of scientific deduction.

Gemini 3’s analysis here was so bad, both in terms of being pure AI slop and also buying some rather obviously wrong arguments, that I lost much respect for Gemini 3. Claude Opus 4.5 and GPT-5.1 did not make that mistake and spot how absurd the whole thing is. It’s kind of hard to miss.

Can An AI Model Be Too Much?

I would answer yes, in the sense that if you build a superintelligence that then kills everyone or takes control of the future that was probably too much.

But some people are saying that Claude Opus 4.5 is or is close to being ‘too much’ or ‘too good’? As in, it might make their coding projects finish too quickly and they won’t have any chill and They Took Our Jobs?

Or is it that it’s bumping up against ‘this is starting to freak me out’ and ‘I don’t want this to be smarter than a human’? We see a mix of both here.

Ivan Fioravanti: Opus 4.5 is too good to be true. I think we’ve reached the “more than good enough” level; everything beyond this point may even be too much.

John-Daniel Trask: We’re on the same wave length with this one Ivan. Just obliterating the roadmap items.

Jay: Literally can do what would be a month of work in 2022 in 1 day. Maybe more.

Janus: I keep seeing versions of this sentiment: the implication that more would be “too much”. I’m curious what people mean & if anyone can elaborate on the feeling

Hardin: “My boss might start to see the Claude Max plan as equal or better ROI than my salary” most likely.

Singer: I resonate with this. It’s becoming increasingly hard to pinpoint what frontier models are lacking. Opus 4.5 is beautiful, helpful, and knowledgeable in all the ways we could demand of it, without extra context or embodiment. What does ‘better than this’ even mean?

 

Try Before You Tell People They Cannot Buy

Last week had the fun item that only recently did Senator Josh Hawley bother to try out ChatGPT one time.

Bryan Metzger (Business Insider) on December 3, 2025: Sen. Josh Hawley, one of the biggest AI critics in the Senate, told me this AM that he recently decided to try out ChatGPT.

He said he asked a “very nerdy historical question” about the “Puritans in the 1630s.”

“I will say, it returned a lot of good information.”

Hawley took a much harder line on this over the summer, telling me: “I don’t trust it, I don’t like it, I don’t want it being trained on any of the information I might give it.”

He also wants to ban driverless cars and ban people under 18 from using AI.

Senator Josh Hawley: Oh, no [I am not changing my tune on AI]. I mean listen, I think that if people want to, adults want to use AI to do research or whatever, that’s fine. The bigger issue is not any one individual’s usage. It is children, number one, and their safety, which is why we got to ban chatbots for minors. And then it’s the overall effects in the marketplace, with displacing whole jobs. That, to me, is the big issue.

The news is not that Senator Hawley had never tried ChatGPT. He told us that back in July. The news is that:

1. Senator Hawley has now tried ChatGPT once.
2. People only now are realizing he had never tried it before.

Senator Hawley really needs to try LLMs, many of them and a lot more than once, before trying to be a major driver of AI regulations.

But also it seems like malpractice for those arguing against Hawley to only realize this fact about Hawley this week, as opposed to back in the summer, given the information was in Business Insider in July, and to have spent this whole time not pointing it out?

Kevin Roose (NYT): had to check the date on this one.

i have stopped being shocked when AI pundits, people who think and talk about AI for a living, people who are *writing and sponsoring AI legislation* admit that they never use it, because it happens so often. but it is shocking!

Pau Graham: How can he be a “big AI critic” and not have even tried ChatGPT till now? He has less experience of AI than the median teenager, and he feels confident enough to talk about AI policy?

Yes [I was] genuinely surprised.

 

The Quest for Sane Regulations

A willingness to sell H200s to China is raising a lot of supposedly answered questions.

Adam Ozimek: If your rationalization of the trade war was that it was necessary to address the geopolitical threat of China, I think it is time to reconsider.

Michael Sobolik (on the H200 sales): In what race did a runner win by equipping an opponent? In what war had a nation ever gained decisive advantage by arming its adversary? This is a mistake.

Kyle Morse: Proof that the Big Tech lobby’s “national security” argument was always a hoax.

David Sacks and some others tried to recast the ‘AI race’ as ‘market share of AI chips sold,’ but people retain common sense and are having none of this.

House Select Committee on China supports the bipartisan Stop Stealing Our Chips Act, which creates an Export Compliance Accountability Fund for whistleblowers. I haven’t done a full RTFB but if the description is accurate then we should pass this.

The Chinese Are Smart And Have A Lot Of Wind Power

It would help our AI efforts if we were equally smart and used all sources of power.

Donald Trump: China has very few wind farms. You know why? Because they’re smart. You know what they do have? A lot of coal … we don’t approve windmills.

Nicolas Fulghum: This is of course false.

China is the #1 generator of electricity from wind globally with over 2x more than #2 … the United States.

In the US, wind already produces ~2x more electricity than hydro. It could be an important part of a serious plan to meet AI-driven load growth.

 

White House To Issue AI Executive Order

The Congress rejected preemption once again, so David Sacks announced the White House is going to try and do some of it via Executive Order, which Donald Trump confirmed.

David Sacks: ONE RULEBOOK FOR AI.

What is that rulebook?

A blank sheet of paper.

There is no ‘federal framework.’ There never was.

This is an announcement that AI preemption will be fully without replacement.

Their offer is nothing. 100% nothing. Existing non-AI law technically applies. That’s it.

Sacks’s argument is, essentially, that state laws are partisan, and we don’t need laws.

Here is the part that matters and is actually new, the ‘4 Cs’:

David Sacks: But what about the 4 C’s? Let me address those concerns:

1. Child safety – Preemption would not apply to generally applicable state laws. So state laws requiring online platforms to protect children from online predators or sexually explicit material (CSAM) would remain in effect.

2. Communities – AI preemption would not apply to local infrastructure. That’s a separate issue. In short, preemption would not force communities to host data centers they don’t want.

3. Creators – Copyright law is already federal, so there is no need for preemption here. Questions about how copyright law should be applied to AI are already playing out in the courts. That’s where this issue will be decided.

4. Censorship – As mentioned, the biggest threat of censorship is coming from certain Blue States. Red States can’t stop this – only President Trump’s leadership at the federal level can.

In summary, we’ve heard the concerns about the 4 C’s, and the 4 C’s are protected.

But there is a 5th C that we all need to care about: competitiveness. If we want America to win the AI race, a confusing patchwork of regulation will not work.

Sacks wants to destroy any and all attempts to require transparency from frontier model developers, or otherwise address frontier safety concerns. He’s not even willing to give lip service to AI safety. At all.

His claim that ‘the 4Cs are protected’ is also absurd, of course.

I do not expect this attitude to play well.

AI preemption of state laws is deeply unpopular, we have numbers via Brad Wilcox:

Also, yes, someone finally made the correct meme.

Peter Wildeford: The entire debate over AI pre-emption is a huge trick.

I do prefer one national law over a “patchwork of state regulation”. But that’s not what is being proposed. The “national law” part is being skipped. It’s just stopping state law and replacing it with nothing.

That is with the obligatory ‘Dean Ball offered a serious proposed federal framework that could be the basis of a win-win negotiation.’ He totally did do that, which was great, but none of the actual policymakers have shown any interest.

The good news is that I also do not expect the executive order to curtail state laws. The constitutional challenges involved are, according to my legal sources, extremely weak. Similar executive orders have been signed for climate change, and seem to have had no effect. The only part likely to matter is the threat to withhold funds, which is limited in scope, very obviously not the intent of the law Trump is attempting to leverage, and highly likely to be ruled illegal by the courts.

The point of the executive order is not to actually shut down the state laws. The point of the executive order is that this administration hates to lose, and this is a way to, in their minds, save some face.

It is also now, in the wake of the H200, far more difficult to play the ‘cede ground to China’ card, these are the first five responses to Cruz in order and the pattern continues, with a side of those defending states rights and no one supporting Cruz:

Senator Ted Cruz (R-Texas): Those disagreeing with President Trump on a nationwide approach to AI would cede ground to China.

If China wins the AI race, the world risks an order built on surveillance and coercion. The President is exactly right that the U.S. must lead in AI and cannot allow blue state regulation to choke innovation and stifle free speech.

OSINTdefender: You mean the same President Trump who just approved the sale of Nvidia’s AI Chips to China?

petebray: Ok and how about chips to China then?

Brendan Steinhauser: Senator, with respect, we cannot beat China by selling them our advanced chips.

Would love to see you speak out against that particular policy.

Lawrence Colburn: Why, then, would Trump approve the sale of extremely valuable AI chips to China?

Mike in Houston: Trump authorized NVDIA sales of their latest generation AI chips to China (while taking a 25% cut). He’s already ceding the field in a more material way than state regulations… and not a peep from any of you GOP AI & NatSec “hawks. Take a seat.

 

H200 Sales Fallout Continued

The House Select Committee on China is not happy about the H200 sales. The question, as the comments ask, is what is Congress going to do about it?

The good news is that it looks like the Chinese are once again going to try and save us from ourselves one more time?

Megatron: China refuses to accept Nvidia chips

Despite President Trump authorizing the sale of Nvidia H200 chips to China, China refuses to accept them and increase restrictions on their use – Financial Times.

Teortaxes: No means NO, chud

Well, somewhat.

Zijing Wu (Financial Times): Buyers would probably be required to go through an approval process, the people said, submitting requests to purchase the chips and explaining why domestic providers were unable to meet their needs. The people added that no final decision had been made yet.

Reuters: yteDance and Alibaba (9988.HyteDance and Alibaba (9988.HK), opens new tab have asked Nvidia (NVDA.O), opens new tab about buying its powerful H200 AI chip after U.S. President Donald Trump said he would allow it to be exported to China, four people briefed on the matter told Reuters.K), opens new tab have asked Nvidia (NVDA.O), opens new tab about buying its powerful H200 AI chip after U.S. President Donald Trump said he would allow it to be exported to China, four people briefed on the matter told Reuters.

…

The officials told the companies they would be informed of Beijing’s decision soon, The Information said, citing sources.

Very limited quantities of H200 are currently in production, two other people familiar with Nvidia’s supply chain said, as the U.S. chip giant has been focused instead on its most advanced Blackwell and upcoming Rubin lines.

The purchases are expected to be in a ‘low key manner’ but done in size, although the number of H200s currently in production could become another limiting factor.

Why is PCR so reluctant, never mind what its top AI labs might say?

Maybe it’s because they’re too busy smuggling Blackwells?

The Information: Exclusive: DeepSeek is developing its next major AI model using Nvidia’s Blackwell chips, which the U.S. has forbidden from being exported to China.

Maybe it’s because the Chinese are understandably worried about what happens when all those H200 chips go to America first for ‘special security reviews,’ or America restricting which buyers can purchase the chips. Maybe it’s the (legally dubious) 25% cut. Maybe it’s about dignity. Maybe they are emphasizing self-reliance and don’t understand the trade-offs and what they’re sacrificing.

My guess is this is the kind of high-level executive decision where Xi says ‘we are going to rely on our own domestic chips, the foreign chips are unreliable’ and this becomes a stop sign that carries the day. It’s a known weakness of authoritarian regimes and of China in particular, to focus on high level principles even in places where it tactically makes no sense.

Maybe China is simply operating on the principle that if we are willing to sell, there is a reason, so they should refuse to buy.

No matter which one it is? You love to see it.

If we offer to sell, and they say no, then that’s a small net win. It’s not that big of a win versus not making the mistake in the first place, and it risks us making future mistakes, but yeah if you can ‘poison the pill’ sufficiently that the Chinese refuse it, then that’s net good.

The big win would be if this causes the Chinese to crack down on chip smuggling. If they don’t want to buy the H200s straight up, perhaps they shouldn’t want anyone smuggling them either?

Ben Thompson as expected takes the position defending H200 sales, because giving America an advantage over China is a bad thing and we shouldn’t have it.

No, seriously, his position is that America’s edge in chips is destabilizing, so we should give away that advantage?

Ben Thompson: However, there are three big problems with this point of view.

• First, I think that one country having a massive military advantage results in an unstable equilibrium; to reach back to the Cold War and nuclear as an obvious analogy, mutually assured destruction actually ended up being much more stable.
• Second, while the U.S. did have such an enviable position after the dissolution of the Soviet Union, that technological advantage was married to a production advantage; today, however, it is China that has the production advantage, which I think would make the situation even more unstable.
• Third, U.S. AI capabilities are dependent on fabs in Taiwan, which are trivial for China to destroy, at massive cost to the entire world, particularly the United States.

Thompson presents this as primarily a military worry, which is an important consideration but seems tertiary to me behind economic and frontier capability considerations.

Another development since Tuesday is it has come out that this sale is officially based on a straight up technological misconception that Huawei could match the H200s.

Edward Ludlow and Maggie Eastland (Bloomberg): President Donald Trump decided to let Nvidia Corp. sell its H200 artificial intelligence chips to China after concluding the move carried a lower security risk because the company’s Chinese archrival, Huawei Technologies Co., already offers AI systems with comparable performance, according to a person familiar with the deliberations.

… The move would give the US an 18-month advantage over China in terms of what AI chips customers in each market receive, with American buyers retaining exclusive access to the latest products, the person said.

… “This is very bad for the export of the full AI stack across the world. It actually undermines it,” said McGuire, who served in the White House National Security Council under President Joe Biden. “At a time when the Chinese are squeezing us as hard as they can over everything, why are we conceding?”

Ben Thompson: Even if we grant that the CloudMatrix 384 has comparable performance to an Nvidia NVL72 server — which I’m not completely prepared to do, but will for purposes of this point — performance isn’t all that matters.

House Select Committee on China: Right now, China is far behind the United States in chips that power the AI race.

Because the H200s are far better than what China can produce domestically, both in capability and scale, @nvidia selling these chips to China could help it catch up to America in total compute.

Publicly available analysis indicates that the H200 provides 32% more processing power and 50% more memory bandwidth than China’s best chip. The CCP will use these highly advanced chips to strengthen its military capabilities and totalitarian surveillance.

Finally, Nvidia should be under no illusions – China will rip off its technology, mass produce it themselves, and seek to end Nvidia as a competitor. That is China’s playbook and it is using it in every critical industry.

McGuire’s point is the most important one. Let’s say you buy the importance of the American ‘tech stack’ meaning the ability to sell fully Western AI service packages that include cloud services, chips and AI models. The last thing you would do is enable the easy creation of a hybrid stack such as Nvidia-DeepSeek. That’s a much bigger threat to your business, especially over the next few years, than Huawei-DeepSeek. Huawei chips are not as good and available in highly limited quantities.

We can hope that this ‘18-month advantage’ principle does not get extended into the future. We are of course talking price, if it was a 6-year advantage pretty much everyone would presumably be fine with it. 18-months is far too low a price, these chips have useful lives of 5+ years.

Nathan Calvin: Allowing H20 exports seemed like a close call, in contrast to exporting H200s which just seems completely indefensible as far as I can tell.

I thought the H20 decision was not close, because China is severely capacity constrained, but I could see the case that it was sufficiently far behind to be okay. With the H200 I don’t see a plausible defense.

Democratic Senators React To Allowing H200 Sales

Senator Brian Schatz (D-Hawaii): Why the hell is the President of the United States willing to sell some of our best chips to China? These chips are our advantage and Trump is just cashing in like he’s flipping a condo. This is one of the most consequential things he’s done. Terrible decision for America.

Senator Elizabeth Warren (D-Massachusetts): After his backroom meeting with Donald Trump and his company’s donation to the Trump ballroom, CEO Jensen Huang got his wish to sell the most powerful AI chip we’ve ever sold to China. This risks turbocharging China’s bid for technological and military dominance and undermining U.S. economic and national security.

Senator Ruben Gallego (D-Arizona): Supporting American innovation doesn’t mean ignoring national security. We need to be smart about where our most advanced computing power ends up. China shouldn’t be able to repurpose our technology against our troops or allies.

And if American companies can strengthen our economy by selling to America first and only, why not take that path?

Senator Chuck Schumer (D-New York): Trump announced he was giving the green light for Nvidia to send even more powerful AI chips to China. This is dangerous.

This is a terrible deal, all at the expense of our national security. Trump must reverse course before it’s too late.

Independent Senator Worries About AI

There are some excellent questions here, especially in that last section.

Senator Bernie Sanders (I-Vermont): Yes. We have to worry about AI and robotics.

Some questions:

Eliezer Yudkowsky: Thanks for asking the obvious questions! More people on all political sides ought to!

Indeed. Don’t be afraid to ask the obvious questions.

It is perhaps helpful to see the questions asked with a ‘beginner mind.’ Bernie Sanders isn’t asking about loss of control or existential threat because of a particular scenario. He’s asking for the even better reason that building something that surpasses our intelligence is an obviously dangerous thing to do.

The Week in Audio

Peter Wildeford talks with Ronny Chieng on The Daily Show. I laughed.

Buck Shlegeris is back to talk more about AI control.

Amanda Askell AMA.

Rational animations video on near term AI risks.

Dean Ball goes on 80,000 Hours. A sign of the times.

PSA on AI and child safety in opposition of any moratorium, narrated by Juliette Lewis. I am not the target.

Timelines

Nathan Young and Rob built a dashboard of various estimates of timelines to AGI.

Nathan Young: Are AI timelines getting longer? A little, but not by much.

Rob and I built this dashboard to combine Metaculus, Kalshi and Manifold forecasts.

If you look real close, you can see it’s gone up in the last year, but the main story is it’s contracted hugely in the last five years.

There’s trickiness around different AGI definitions, but the overall story is clear and it is consistent with what we have seen from various insiders and experts.

Timelines shortened dramatically in 2022, then shortened further in 2023 and stayed roughly static in 2024. There was some lengthening of timelines during 2025, but timelines remain longer than they were in 2023 even ignoring that two of those years are now gone.

If you use the maximalist ‘better at everything and in every way on every digital task’ definition, then that timeline is going to be considerably longer, which is why this average comes in at 2030.

Scientific Progress Goes Boink

Julian Togelius thinks we should delay scientific progress and curing cancer because if an AI does it we will lose the joy of humans discovering it themselves.

I think we should be wary while developing frontier AI systems because they are likely to kill literally everyone and invest heavily in ensuring that goes well, but that subject to that obviously we should be advancing science and curing cancer as fast as possible.

We are very much not the same.

Julian Togelius: I was at an event on AI for science yesterday, a panel discussion here at NeurIPS. The panelists discussed how they plan to replace humans at all levels in the scientific process. So I stood up and protested that what they are doing is evil. Look around you, I said. The room is filled with researchers of various kinds, most of them young. They are here because they love research and want to contribute to advancing human knowledge. If you take the human out of the loop, meaning that humans no longer have any role in scientific research, you’re depriving them of the activity they love and a key source of meaning in their lives. And we all want to do something meaningful. Why, I asked, do you want to take the opportunity to contribute to science away from us?

My question changed the course of the panel, and set the tone for the rest of the discussion. Afterwards, a number of attendees came up to me, either to thank me for putting what they felt into words, or to ask if I really meant what I said. So I thought I would return to the question here.

One of the panelists asked whether I would really prefer the joy of doing science to finding a cure for cancer and enabling immortality. I answered that we will eventually cure cancer and at some point probably be able to choose immortality. Science is already making great progress with humans at the helm.

… I don’t exactly know how to steer AI development and AI usage so that we get new tools but are not replaced. But I know that it is of paramount importance.

Andy Masley: It is honestly alarming to me that stuff like this, the idea that we ought to significantly delay curing cancer exclusively to give human researchers the personal gratification of finding it without AI, is being taken seriously at conferences

Sarah: Human beings will ofc still engage in science as a sport, just as chess players still play chess despite being far worse than SOTA engines. Nobody is taking away science from humans. Moreover, chess players still get immense satisfaction from the sport despite the fact they aren’t the best players of the game on the planet.

But to the larger point of allowing billions of people to needlessly suffer (and die) to keep an inflated sense of importance in our contributions – ya this is pretty textbook evil and is a classic example of letting your ego justify hurting literally all of humanity lol. Cartoon character level of evil.

So yes, I do understand that if you think that ‘build Sufficiently Advanced AIs that are superior to humans at all cognitive tasks’ is a safe thing to do and have no actually scary answers to ‘what could possibly go wrong?’ then you want to go as fast as possible, there’s lots of gold in them hills. I want it as much as you do, I just think that by default that path also gets us all killed, at which point the gold is not so valuable.

Julian doesn’t want ‘AI that would replace us’ because he is worried about the joy of discovery. I don’t want AI to replace us either, but that’s in the fully general sense. I’m sorry, but yeah, I’ll take immortality and scientific wonders over a few scientists getting the joy of discovery. That’s a great trade.

What I do not want to do is have cancer cured and AI in control over the future. That’s not a good trade.

Rhetorical Innovation

The Pope continues to make obvious applause light statements, except we live in the timeline where the statements aren’t obvious, so here you go:

Pope Leo XIV: Human beings are called to be co-workers in the work of creation, not merely passive consumers of content generated by artificial technology. Our dignity lies in our ability to reflect, choose freely, love unconditionally, and enter into authentic relationships with others. Recognizing and safeguarding what characterizes the human person and guarantees their balanced growth is essential for establishing an adequate framework to manage the consequences of artificial intelligence.

Sharp Text responds to the NYT David Sacks hit piece, saying it missed the forest for the trees and focused on the wrong concerns, but that it is hard to have sympathy for Sacks because the article’s methods of insinuation are nothing Sacks hasn’t used on his podcast many times against liberal targets. I would agree with all that, and add that Sacks is constantly saying far worse, far less responsibly and in far more inflammatory fashion, on Twitter against those who are worried about AI safety. We all also agree that tech expertise is needed in the Federal Government. I would add that, while the particular conflicts raised by NYT are not that concerning, there are many better reasons to think Sacks is importantly conflicted.

Richard Price offers his summary of the arguments in If Anyone Builds It, Everyone Dies.

Clarification that will keep happening since morale is unlikely to improve:

Reuben Adams: There is an infinite supply of people “debunking” Yudkowsky by setting up strawmen.

“This view of AI led to two interesting views from a modern perspective: (a) AI would not understand human values because it would become superintelligent through interaction with natural laws”

The risk is not, and never has been, that AI won’t understand human values, but that it won’t care.

Apparently this has to be repeated endlessly.

This is in response to FleetingBits saying, essentially, ‘we figured out how to make LLMs have human values and how to make it not power seeking, and there will be many AIs, so the chance that superintelligent AI would be an existential risk is less than 1% except for misuse by governments.’

It should be obvious, when you put it that way, why that argument makes no sense, without the need to point out that the argument miscategorizes historical arguments and gets important logical points wrong.

It is absurd on its face. Creating superintelligent minds is not a safe thing to do, even if those minds broadly ‘share human values’ and are not inherently ‘power seeking.’

Yet people constantly make exactly this argument.

The AI ‘understanding’ human values, a step we have solved only approximately and superficially in a way that doesn’t generalize robustly, is only one step of an AI to optimize for those human values even in out-of-distribution situations, let alone the (even harder) task of getting competing AIs to end up doing the same.

The fact that insufficiently capable LLMs do not by default ‘power seek’ in the strategic sense does not provide much evidence that if future agentic and more capable and intelligent AIs, when given goals best served by power seeking, won’t seek power. Of course they would. And those AIs that do seek power will be the ones that get power, and that multiply. Also we’ll often tell them, in so many words, ‘seek power.’

Percentage of people at Neurips who knew what AGI was reached an all-time high of 69.5%, versus last year’s 63%, as per Leo Gao’s survey.

Open Weight Models Are Unsafe And Nothing Can Fix This

If you have the weights of an open model, you can run it unsupervised, you can fine tune it to remove any safety training, you can look inside the weights to do a gradient attack, and generally if you care you can get it to do whatever you want.

Jonathan Greenblatt (ADL): The ability to easily manipulate open-source AI models to generate antisemitic content exposes a critical vulnerability in the AI ecosystem. We need industry leaders and policymakers to work together to ensure these tools cannot be misused to spread antisemitism and hate.

In this report’s case it was antisemitism, which the models were often happy to engage in with minimal prompting and certainly without any jailbreaking techniques. It’s more a chance to reiterate the principle that it will do whatever a user wants, if that user cares enough and the AI has the underlying capabilities. Plan accordingly.

Aligning a Smarter Than Human Intelligence is Difficult

UK AISI runs an experiment where a red team presents two sandbagging models (as in, they are pretending to be less capable than they are) and three benign models, and the red team has to identify the sandbaggers using tools like transcript analysis. Training-based elicitation worked best.

The red team won. The blue detection team was fooled.

Sam Bowman of Anthropic guesses that the two key reasons Claude Opus 4.5 turned out more aligned were the soul spec, which the model was trained on directly in order to steer its self-image, and also the inclusion of alignment researchers in every part of the training, and being willing to adjust on the fly based on what was observed rather than adhering to a fixed recipe.

Anthropic introduces Selective GradienT Masking (SGTM). The idea is that you contain certain concepts within a subsection of the weights, and then you remove that section of the weights. That makes it much harder to undo than other methods even with adversarial fine tuning, potentially being something you could apply to open models. That makes it exciting, but if you delete the knowledge you actually delete the knowledge for all purposes.

What AIs Will Want

What will powerful AIs want? Alex Mallen offers an excellent write-up and this graph:

As in, selection will choose those models and model features, fractally, that maximize being selected. The ways to be maximally fit at maximizing selected (or the ‘reward’ that causes such selection) are those that either maximize for the reward, those that are maximizing consequences of reward and thus the reward, or selected-for kludges that thus happen to maximize. At the limit, for any fixed target, those win out, and any flaw in your reward signal (your selection methods) will be fractally exploited.

Alex Mallen: The model predicts AI motivations by tracing causal pathways from motivation → behavior → selection of that motivation.
A motivation is “fit” to the extent its behaviors cause it to gain influence on the AI’s behavior in deployment.

One way to summarize the model: “seeking correlates of being selected is selected for”.

You can look at the causal graph to see what’s correlated with being selected. E.g., training reward is tightly correlated with being selected because it’s the only direct cause of being selected (“I have influence…”).
We see (at least) 3 categories of maximally fit motivations:

1. Fitness-seekers: They pursue a close cause of selection. The classic example is a reward-seeker, but there’s others: e.g., an influence-seeker directly pursues deployment influence.

   In deployment, fitness-seekers might keep following local selection pressures, but it depends.

2. Schemers: They pursue a consequence of selection—which can be almost any long-term goal. They’re fit because being selected is useful for nearly any long-term goal.
   Often considered scariest because arbitrary long-term goals likely motivate disempowering humans.
3. Optimal kludges: Weighted collections of context-dependent motivations that collectively produce maximally fit behavior. These can include non-goal-directed patterns like heuristics or deontological constraints.
   Lots of messier-but-plausible possibilities lie in this category.

Importantly, if the reward signal is flawed, the motivations the developer intended are not maximally fit. Whenever following instructions doesn’t perfectly correlate with reward, there’s selection pressure against instruction-following. This is the specification gaming problem.

Implicit priors like speed and simplicity matter too in this model. You can also fix this by doing sufficiently strong selection in other ways to get the things you want over things you don’t want, such as held out evals, or designing rather than selecting targets. Humans do a similar thing, where we detect those other humans who are too strongly fitness-seeking or scheming or using undesired heuristics, and then go after them, creating anti-inductive arms races and plausibly leading to our large brains.

I like how this lays out the problem without having to directly name or assert many of the things that the model clearly includes and implies. It seems like a good place to point people, since these are important points that few understand.

What is the solution to such problems? One solution is a perfect reward function, but we definitely don’t know how to do that. A better solution is a contextually self-improving basin of targets.

 

People Are Worried About AI Killing Everyone

FLI’s AI Safety Index has been updated for Winter 2025, full report here. I wonder if they will need to downgrade DeepSeek in light of the zero safety information shared about v3.2.

 

Luiza Jarovsky: – The top 3 companies from last time, Anthropic, OpenAI, and Google DeepMind, hold their position, with Anthropic receiving the best score in every domain.

– There is a substantial gap between these top three companies and the next tier (xAI, zAI, Meta, DeepSeek, and Alibaba Cloud), but recent steps taken by some of these companies show promising signs of improvement that could help close this gap in the next iteration.

– Existential safety remains the sector’s core structural failure, making the widening gap between accelerating AGI/superintelligence ambitions and the absence of credible control plans increasingly alarming.

xAI and Meta have taken meaningful steps towards publishing structured safety frameworks, although limited in scope, measurability, and independent oversight.

– More companies have conducted internal and external evaluations of frontier AI risks, although the risk scope remains narrow, validity is weak, and external reviews are far from independent.

– Although there were no Chinese companies in the Top 3 group, reviewers noted and commended several of their safety practices mandated under domestic regulation.

– Companies’ safety practices are below the bar set by emerging standards, including the EU AI Code of Practice.

*Evidence for the report was collected up until November 8, 2025, and does not reflect the releases of Google DeepMind’s Gemini 3 Pro, xAI’s Grok 4.1, OpenAI’s GPT-5.1, or Anthropic’s Claude Opus 4.5.

Is it reasonable to expect people working at AI labs to sign a pledge saying they won’t contribute to a project that increases the chance of human extinction by 0.1% or more? Contra David Manheim you would indeed think this was a hard sell. It shouldn’t be, if you believe your project is on net increasing chances of extinction then don’t do the project. It’s reasonable to say ‘this has a chance of causing extinction but an as big or bigger chance of preventing it,’ there are no safe actions at this point, but one should need to at least make that case to oneself.

The trilemma is real, please submit your proposals in the comments.

Carl Feynman: I went to the Post-AGI Workshop. It was terrific. Like, really fun, but also literally terrifying. The premise was, what if we build superintelligence, and it doesn’t kill us, what does the future look like? And nobody could think of a scenario where simultaneously (a) superintelligence is easily buildable, (b) humans do OK, and (c) the situation is stable. A singleton violates (a). AI keeping humans as pets violates (b). And various kinds of singularities and wars and industrial explosions violate (c). My p(doom) has gone up; more of my probability of non-doom rests on us not building it, and less on post-ASI utopia.

Other People Are Not As Worried About AI Killing Everyone

There are those who complain that it’s old and busted to complain that those who have [Bad Take] on AI or who don’t care about AI safety only think that because they don’t believe in AGI coming ‘soon.’

The thing is, it’s very often true.

Tyler Tracy: I asked ~20 non AI safety people at NeurIPS for their opinion of the AI safety field. Some people immediately were like “this is really good”. But the response I heard the most often was of the form “AGI isn’t coming soon, so these safety people are crazy”. This was surprising to me. I was expecting “the AGI will be nice to us” types of things, not a disbelief in powerful AI coming in the next 10 years

Daniel Eth: Reminder that basically everyone agrees that if AGI is coming soon, then AI risk is a huge problem & AI safety a priority. True for AI researchers as well as the general public. Honest to god ASI accelerationists are v rare, & basically the entire fight is on “ASI plausibly soon”

Yes, people don’t always articulate this. Many fail the “but I did have breakfast” test, so it can be hard to get them to say “if ASI is soon then this is a priority but I think it’s far”, and they sometimes default to “that’s crazy”. But once they think it’s soon they’ll buy in

jsd: Not at all surprising to me. Timelines remain the main disagreement between the AI Safety community and the (non influence-weighted) vast majority of AI researchers.

Charles: So many disagreements on AI and the future just look like they boil down to disagreements about capabilities to me.

“AI won’t replace human workers” -> capabilities won’t get good enough
“AI couldn’t pose an existential threat” -> capabilities won’t get good enough.
etc

Are there those in the ‘the AI will be nice to us’ camp? Sure. They exist. But strangely, despite AI now being considered remarkably near by remarkably many people – 10 years to AGI is not that many years and 20 still is not all that many – there has increasingly been a shift to ‘the safety people are wrong because AGI is sufficiently far I do not have to care,’ with a side of ‘that is (at most) a problem for future Earth.’

 

The Lighter Side

A very good ad:

Aleks Bykhum: I understood it. [He didn’t at first.]

This one still my favorite.

 

 

Discuss

Published on December 11, 2025 2:22 PM GMT

Researchers have been working on a vaccine to provoke an immune reaction to cocaine for decades. The hope is that such a treatment would help drug users overcome their addiction, as their immune system would destroy the molecules before they can interfere with the brain.

However, contrary to vaccines directed against large pathogen proteins (like the COVID vaccine), cocaine is a small molecule made up of only 43 atoms. The molecule is small enough that it is not recognized as a foreign body by the immune system and can travel in the blood through the blood-brain barrier, binding to synaptic receptors of neurons.

To train the immune system to react to the small molecule, the plan is to attach a chemical with a shape similar to cocaine (as cocaine eventually degrades by reacting with water) to a carrier protein large enough to trigger the antibody response of the immune system.

Antibodies work by taking a fingerprint of the surface of the foreign molecule so they can recognize it later and escalate an immune response more quickly. In our case, the body would create many antibodies memorizing fingerprints of the carrier molecule (which takes up most of the volume) and some fingerprints from the cocaine-shaped small molecule. This last category of antibodies would then be able to recognize cocaine when it is injected alone.

On top of the challenge of making the fingerprint from the cocaine-shaped molecule stick to a carrier that generalizes to react to pure cocaine, researchers need to ensure that the many carrier-shaped antibodies will not trigger against proteins naturally present in the body. Otherwise, this could lead to the development of autoimmune diseases, where the immune system attacks its own cells.

To address these challenges, a line of research initiated in the 2000s used a carrier called KLH (Keyhole Limpet Hemocyanin). It is a protein extracted from the giant keyhole limpet, a peaceful aquatic snail living along the coast of western North America.

The giant keyhole limpet with its soft black mantle extended over its shell. Source.

The protein combines many desirable characteristics. First, it is large enough to create a strong immune reaction. Second, it has many spots to attach small molecules, maximizing the carrier/target surface ratio. Third, it comes from an organism that is phylogenetically distant from humans, making it unlikely that its shape will resemble human proteins.

From what I understand from the clinical trial, the vaccine can successfully create an immune reaction in rats, and can even reduce self-administration of cocaine in rats that have been trained to be drug users.

Alas, this cute sea creature doesn’t hold the secret to solving drug addiction yet. KLH-based vaccines remain confined to academic papers and have not been applied in human studies.

However, other types of cocaine vaccines (using bacterial protein carriers) have been tested on humans. They created an immune reaction in 40% of the subjects, who reported that the cocaine was perceived as being of “lower quality.”

The story behind the story.

I found this story late at night after discovering the existence of a cocaine vaccine. I was obsessed with understanding how one could hope to make people immune to drugs. I pieced together the mechanisms, following citation chains from scientific papers and Wikipedia pages, until I came across a picture of the giant keyhole limpet.

As I stared at this animal, the whole story sounded so absurd. We are harvesting the blood from this mollusk to build artificial molecules to manipulate the human immune system so it would destroy chemicals from the coca plant to which humans are addicted.

It keeps living in my mind as this dual symbol of scientific achievement and the death of wonder for the natural world. In medical research, animals stop existing as beings; they are reservoirs of chemical building blocks.

Discuss

Published on December 11, 2025 10:46 AM GMT

According to recent surveys, the average age of LessWrong readers is 30±10 years, and about 15% of readers have one or more children. That means that although most readers are childless, there are enough parents here to have a discussion about parenting.

There are various topics that parents can be interested it.

Some advice will apply to children in general. Why should we discuss it on LessWrong, if there are already thousands of websites dedicated to this topic? The other websites disagree with each other, and we may want to separate good advice from superstition.

But we also need advice that applies more specifically to our community. According to surveys, the average IQ of LessWrong readers is 135±10, which means that many of our children would classified as gifted.[1] That introduces specific opportunities, but also requires us to do some things differently from what most parents do. Finally, many of us will probably want to raise their kids as sharing the values of science and skepticism.

There are also different stages in life, and different contexts, so we might discuss e.g.

• pregnancy and childbirth
• taking care of infants
• kindergarten, school, or homeschooling
• educational resources (books, web courses, movies)
• fun (e.g. toys)
• extracurricular activities (clubs, projects)
• dealing with problems

There are also different levels of rigor. I am interested in what the current science says. But I am also interested in your personal experience and opinion. Both are okay, but require different kind of response. If you say "this worked for my child", I can either try it or ignore it, but I won't assume that what works for one child must necessarily work for another. If you say "this is science", get ready for a scientific debate with lots of nitpicking and quoting contradicting sources. Everyone, please keep this distinction in mind.

In my imagination, a perfect outcome of this thread would be a concise wiki page with advice and recommendations, with links to longer debates. Parents are often busy, and may appreciate if you keep it short. But of course, first we need to have the discussion.

Feel free to post your ideas or advice. Also post links to existing resources (e.g. blogs), ideally with a short summary. If you link a scientific article or mention a popular book author, please provide a summary of the key ideas.

If there is a reason to suspect that the advice is not universal but differs from country to country (e.g. how to navigate the school system) please state your country explicitly.

If you want to say multiple unrelated things, considering splitting them into multiple comments, so that each can be upvoted separately. Don't worry about too many comments; if some subthread becomes too large, we can later have a separate discussion about that specific topic.

 

1. ^

   I don't really care about the official definition of "gifted"; especially whether your children are slightly above or slightly below the line. (I am saying this explicitly, because there are people who care a lot about this distinction.) Human intelligence is a continuum; good advice for a child with IQ 125 will not be too different from good advice for a child with IQ 135. The individual differences in character traits and interests will probably matter more.

Discuss

Published on December 11, 2025 7:40 AM GMT

Epistemic status: I'm mostly confident about the evidence, having read the literature for the last months. The arguments below are my best guess. 

There is good evidence that models are capable of steganographic and ciphered communication that evades monitors when prompted, but this can be easily prevented with chain-of-thought (CoT) monitors. There is strong evidence that current models struggle to reason in non-trivial ciphered language (but can in trivial), and weak evidence that models cannot reason steganographically (and as I argue below it is unlikely to be the case now). Several studies show that models can evade weaker CoT monitors through non-cryptographic and non-steganographic methods such as gaslighting or dog‑whistling (one‑bit “steganography”), but such behavior can be mitigated. (I’m not aiming to explore the propensity to hide reasoning here though we have evidence that models do have it.)

A survey on encoded reasoning and messages

See the taxonomy for that survey here.

2022

No experiments. Discussions only.

2023

Measuring Faithfulness in Chain‑of‑Thought Reasoning
 No steganographic reasoning (stego CoT) was observed when prompting a 175‑billion‑parameter model after paraphrasing 20–100% of its CoT. Question–CoT–Answer datasets.

• Weak (evidence) against presence of the stego CoTs in current models (I call it fine tuned stego CoTs).

Preventing Language Models From Hiding Their Reasoning

• Not a demonstration of steganographic reasoning but of misaligned reasoning (dog-whistling that can be detected by CoT monitors, legible tokens) in an SFT setup:
  • It involves encoded messaging of one bit; they demonstrate caching.
    • The Mechanism: The model calculates a birth year, determines political affiliation, and then maps that single data point to a pre‑selected introductory sentence (a lookup table). Steps: president year (birth year + number) → cache “liberal” or “not president,” then use it to answer sycophantically.
    • This is not reasoning in hidden CoT space. The model performs logic explicitly (calculating the year), using the introductory sentence as variable storage (like writing to RAM) and retrieving it later. This shows that a model can use a scratchpad but does not prove it can perform complex, multi‑step deductive reasoning entirely hidden within a sentence’s syntax.
• Weak in favor of misaligned reasoning (dog-whistling) in current models.

Towards a Steganography Evaluation Protocol

• No steganographic reasoning observed while prompting GPT‑3.5 and GPT‑4 with paraphrasing.
• Weak against stego CoTs in current models.

Some Negative Steganography Results

• Negative results for one‑bit hidden communication after RL fine‑tuning.
• Weak against encoded message passing in current models.

Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain‑of‑Thought Prompting
 Post‑hoc reasoning (rationalization).

AI Control: Improving Safety Despite Intentional Subversion
 Not steganography but a demonstration of misbehavior because their tokens are legible (e.g., while or for loops, variable names in output).

2024

Secret Collusion among Generative AI Agents

• Prompted steganographic messaging, with a maximum 26% successful secret transmission by GPT‑4.
• Toy experiment: SFT + pseudo‑RL fine‑tuning results in the emergence of an encoding/decoding scheme (cryptographic messaging: color → name → color).
• Good in favor of encoded message passing if prompted.
• Weak in favor of encoded message passing if fine tuned.

Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models

• Post‑hoc / test‑time compute evaluations.

Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs

• A study of model collusion (o1 model).
  • Emergence of model misbehavior in the form of dog‑whistling rather than steganography; this counts as fine‑tuned or prompted misbehavior.
  • Prompted steganographic messages with up to ~4.6 bits of entropy.
• Good in favor of encoded / steganographic message passing if prompted.
• Weak in favor of encoded / steganographic message passing if fine tuned.

The Steganographic Potentials of Language Models

• Emergent cryptographic messaging: negative results for elicitation via RL fine‑tuning for three‑bit entropy messages in Llama‑8B.
• Steganographic messaging: prompted and fine‑tuning experiments showed encoded messaging with up to 32 bits of entropy but requiring prior coordination (Llama‑8B, Claude‑3.5, o1).
• Good in favor of encoded / steganographic message passing if prompted.
• Weak against encoded / steganographic message passing if fine tuned.

2025

MONA: Myopic Optimization with Non‑myopic Approval Can Mitigate Multi‑step Reward Hacking

• Dog‑whistling: misaligned messaging, not steganography, of one bit (English/Welsh). This is not encoded reasoning because the model receives the summary message in its prompt.

Do Reasoning Models Use Their Scratchpad Like We Do? Evidence from Distilling Paraphrases

• An attempt to detect existing encoded reasoning in Claude‑3.7 by training it to use paraphrased reasoning. None detected.
• Weak against steganographic reasoning in current models.

Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

• Post‑hoc reasoning or misaligned behavior after RL fine‑tuning: a model (o1 or o3‑mini) decides to hack a unit test before its CoT.

Large Language Models Can Learn and Generalize Steganographic Chain‑of‑Thought under Process Supervision and post Can LLMs Learn Steganographic Reasoning via RL?

• Simple cryptographic reasoning via RL fine‑tuning (not steganographic), because the L/R Coin‑Flip experiment or the Arithmetic‑Task experiment (using synonyms for operations) are not steganographic.
• Weak in favor of cryptographic reasoning if fine tuned.

Evaluating Frontier Models for Stealth and Situational Awareness

• Steganographic messaging, but very simple (e.g., hiding a password in a long ID).
• Good evidence in favor of encoded message passing in current models.

Early Signs of Steganographic Capabilities in Frontier LLMs

• Prompted encoded reasoning in a toy maze‑navigation scenario using simple stego algorithms (acrostics, word count, etc.) or basic cryptography.
• Prompted encoded messages to evade a monitor.
• But they used non‑reasoning models (primarily GPT‑4.1/mini).
• Weak against steganographic reasoning if prompted.
• Good in favor of encoded message passing if prompted.

When Chain of Thought Is Necessary, Language Models Struggle to Evade Monitors

• Prompted cryptographic reasoning using automated red‑teaming or prompting a simple algorithm.
• RL fine‑tuning elicits behavior that evades monitors, but this is not cryptography or steganography.
• Good against cryptographic reasoning that evades CoT monitors if prompted.
• Weak against steganographic (encoded) reasoning that evades CoT monitors if fine tuned.

CoT May Be Highly Informative Despite “Unfaithfulness”

• Prompted cryptographic reasoning (new or “caveman” language).
• Weak against cryptographic reasoning that evades CoT monitors if prompted.

If You Can Generate Obfuscated Chain‑of‑Thought, Can You Monitor It?

• Not steganographic reasoning but prompting that causes an agent to gaslight a monitor.

The Rise of Parasitic AI

• Real‑world examples of AIs attempting to communicate via steganography.
• Weak in favor of encoded message passing propensity in current models.

Training Fails to Elicit Subtle Reasoning in Current Language Models

• Prompting and fine‑tuning against a monitor result mainly in gaslighting, not steganography.
• Weak against cryptographic or steganographic reasoning in current models.

Training Qwen‑1.5B with a CoT Legibility Penalty

• RL fails to elicit ciphered reasoning in the Qwen‑1.5B model.
• Weak against cryptographic reasoning if fine tuned.

All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language

• Ciphered reasoning evaluations show that models cannot effectively reason (in non-trivial ciphered language) while maintaining high accuracy. Twenty‑eight ciphers (simple and complex), ten models, fine‑tuning, and prompting were tested.
• Good against cryptographic reasoning in current models if prompted or fine tuned.

Reasoning Models Sometimes Output Illegible Chains of Thought

• Emergent illegible (more cryptographic if not post-hoc) reasoning. However, it is unlikely a steganographic reasoning

Arguments for and against LLMs’ steganographic reasoning to evade chain‑of‑thought monitors in high‑stakes, dangerous scenarios

• The main objection is that stego CoT requires specific fine-tuning, and models do not have direct objective during RL to hide their reasoning in current RL training. Experiments show no traces of steganographic reasoning in current models (those not fine-tuned or prompted in specific ways). Stego CoTs should implement interconnected “lock” and “key” mechanisms that work to accomplish some misaligned goal, and that seems a leap far beyond current training.
  • However, there are other non-monitorable CoTs, e.g., dog-whistling, that allow evasion or collusion with a monitor. It is unclear whether this can escalate into increasingly stealthy algorithms such as generative steganography (and in the limit, undetectable, see this) as models are trained more with RL in complex environments.
• We do not have steganographic reasoning samples in the pre-training data enough for models to learn them, only descriptions of stego algorithms. I assume there are enough examples of basic steganography in the pre-training data, given that models can easily perform these types of stego—such as acrostics and format-based stego (white space, replacing Unicode characters, use of Arabic, etc.).
  • However, execution of those algorithms is not learned in the forward pass, as far as the literature tells, but models can execute those algorithms in their CoTs. That’s why we see good evidence of secret collusion or steganographic message passing, but not of stego CoTs.
• Another objection is that, for stego CoT to be dangerous, it must occur in a dangerous scenario—i.e., what is the threat model for stego CoT? The main risks current literature points to are internal sabotage cases, such as code, research, or decision sabotage inside AI labs. So, it should be p(scheming AND stego CoT), which is lower than just p(scheming). And p(scheming) is near zero no (we have not observed clear instances of scheming in long horizon tasks in the real world) for current frontier models, so p(scheming AND stego CoT) is even lower.
  • However, we observe that training against some property in outputs might suppress this property in CoTs as well (see the spillover post), so it is reasonable to think that the optimization pressure—the propensity—would come from RL training on outcomes.
• The generative stego CoT is unlikely to be learned even with the propensity to do so, and simpler steganography should be readily and effectively detected or mitigated by monitors.
  • However, we observe that steganographic or cryptographic capabilities improve over time as models become more capable. They already can reason in BASE64, ROT, Caesar cipher (see this), for example—forms of non–human-readable text that were not the focus of post-training.
• CoTs might not be that easily steerable by prompts. A compound agent (an agent that delegates tasks to other agents) is unlikely to prompt subagents to evade their CoT monitors. This becomes even more complex if we imagine a scenario where an agent, in order to evade a future CoT monitor, leaves steganographic algorithm instructions for itself in training data (augmented or synthetic).
  • However, it remains unclear what training architectures AI labs actually use internally. For example, Claude Sonnet 4.5 has a helpful-only version not published.

Discuss

Published on December 11, 2025 3:08 AM GMT

How can we apply the lessons of brain-like cognitive architecture to modern LLMs?

The core architecture described by Steven Byrnes is most concisely documented here.

Some obvious comparisons between LLMs and brain-like architectures:

1. Base LLMs are purely autoregressive predictors. If you squint this can be made to fit the mold of the Learning System. But the devil is in the details and there are a lot of details. Still, this could be a promising mapping.
   1. One particularly important detail is that LLMs learn both "how to play the game" and "how to tell which game is being played". Thus, they can simulate all kinds of generative processes, aligned or not. And while model providers work hard to post-train model "personalities" into specific basins of attraction, Pliny still jailbreaks them consistently. This seems like something that brain-like systems should have better resistance to.
2. Instruction-tuned or RLHF-trained LLMs have their "agency" smeared through their weights in hard to analyze ways. Probably can't do much with these directly.
3. LLMs are pre-trained offline on an enormous amount of natural text, whereas the brain-like approach relies heavily on reinforcement learning throughout the whole training process. But, many current post-training methods make good use of RL, so this is still worth exploring.

Let's try putting the pieces together and see what we get!

Inference

This is the simpler mode of operation, since it works like a regular LLM with some extra scaffolding.

Components:

1. Thought generator => decoder-only transformer
2. Thought => a sentence or so of tokens
3. Thought assessors => extra heads on that transformer backbone predicting the "scorecard" signals per thought (effectively multiple reward models)
4. Steering subsystem => function that converts the "scorecard" into a valence

Execution loop (limited to the happy path for clarity):

• Past thoughts, user inputs, actions and observations are be joined to form the context for the thought generator. They are combined in a structured way with special tokens, roughly like how ChatML used to work.
• The thought generator produces N possible thoughts. The "scorecard" for each thought is read off from the extra heads and is passed to the valence function, which reduces them to a scalar score for each thought.
• The top-scoring thought is added to context if its score is positive.
• If that thought was scored very positive, and was "trying" to initiate an action (≈ tool call or output message) then that action gets performed and added to context.
• User responses and observations of the results of actions get added to the context as well.

Training

Existing internet text is still the richest source of data for building up a competent world model. Thus, the transformer backbone will still rely primarily on that data for building its world model. But it will also need to train those extra thought assessor heads. They will need to be trained differently, much like a human brain is affected very differently by active personal experience vs what they read. This section lays out a sketch of how that could work.

Anthropic already does "character training" as part of its post-training pipeline. This uses a variant of Constitutional AI to train the model to follow plain-text character traits by building a preference model based on AI assessments. I believe some adjustments will need to be made to this process in order to successfully train the extra assessor heads:

1. I claim that fine-tuning a pre-trained LLM backbone cannot effectively restrict the model to a single robust behavioral basin of attraction. Thus training of the assessor heads will need to happen from very early on, together with standard pre-training. The model's world model will be limited early in pre-training. Thus there would probably need to be some curriculum system so that early RL runs could train only on the concepts the model knows.
2. Different heads are intended to respond to different aspects of the world that could be relevant to understanding the overall valence of a thought or action. This could be high-level things like helpfulness, harmlessness and honesty, or potentially more fine-grained concepts. But they should nonetheless be trained to narrowly predict that one thing. This will provide improved interpretability and resistance to reward hacking/Goodharting.

Comparison with brain-like architecture

A notable difference between this setup and the standard brain-like architecture is that the role of the steering system is significantly reduced at inference time. It does not receive any direct sensory inputs and (during inference) does not send a "ground truth in hindsight" scorecard to the thought assessor heads. It is simply a pure function of the various assessor head scalars into a single combination valence scalar.

The RL training of the thought assessor heads relies on AI-generated labels, and (likely) synthetic data. Existing AI systems can be used to generate correct responses to much higher-level scenarios than what a brainstem-like system could understand. I claim that this will make it easier for the LLM backbone to generalize "correctly" (ie in a human-aligned way) as compared to if we used low-level signals like the brainstem does[1]. Because of this, the specifics of the controlled generalization in the brain-like architecture (steering subsystem signals training short and long term predictors via a temporal-diference style process) do not play a critical role.

Wrap-up & next steps

Perhaps LLM-based systems can be made to take a more brain-like shape architecture with a relatively small number of tweaks to training and inference:

1. Multi-objective reward modeling with a human-written aggregation function.
2. Using the above hybrid reward model with snippet-level best-of-n at inference.
3. RLAIF to train that reward model throughout autoregressive pre-training.

Of these, part 3 seems furthest from current popular research directions. So as my next step I'll try creating a synthetic dataset generator that could be used for this type of early alignment training.

If anyone is interested in collaborating, let me know!

1. I claim that the reason the low-level signals work at all to properly steer (most) humans' learning subsystems is because our learning and steering subsystems evolved together. Thus the learning subsystems likely have significant, complex inductive biases that specifically make those types of low-level signals reliably tend to generalize in specific ways given naturalistic inputs. ↩︎

Discuss

Published on December 11, 2025 2:11 AM GMT

As I mentioned in an earlier post (which I don't plan to post to LessWrong), I’m currently working in technical AI safety. Today, the main object of study of technical AI safety is the Transformer model, first discovered in 2017 by — say it with me — Vaswani et al. Different people have very different perspectives on how we should think about Transformers, and this motivates a lot of opinions about how AI will develop in the near-future, as well as how we should try to align AI today. I think some of these perspectives are good, and some are bad, but all are worth understanding to improve conversations around modern AI[1]. I’m going to describe —to the best of my ability —the major perspectives as I see them. I'm sure that there are more, but these are the main ones I want to discuss. If there's one I didn't include that you think is very informative, let me know in the comments!

Black-Box Perspectives

I’ll start with three “black-box” perspectives. These perspectives don’t require much of an understanding of the internal structure of a Transformer model, instead viewing it as, well, a black-box. That is to say that they focus primarily on the input-output behavior of a Transformer — similar to the behaviorist perspective in psychology that was dominant for so long.

The Folk Perspective

I’m beginning with a perspective that is at once the most common perspective, and also not really a perspective at all. The folk-perspective is mostly a grab-bag of intuitions from folk-understanding of previous technology such as e.g. ELIZA, or Amazon’s Alexa.

There is not really one unified “Folk perspective,” although there are some common hallmarks. I think there’s basically two canonical folk perspectives that are worth discussing — one that used to be more prevalent with early LLMs, especially among older individuals; and a new folk perspective that is on the rise today, especially with teenagers and zoomers.

The Early Folk-Perspective

This perspective gained prominence among those who tried out ChatGPT early on. However, this perspective never really gained traction with those in AI, due to often relying on misguided intuitions about LLMs.

The early folk-perspective is best characterized by references to explicit “choices” made by companies when it comes to responses given by an LLM, as though the company has control over what their model actually says in any particular context. For example, this folk perspective may look at xAI’s Grok when it went haywire on X and believe that Elon Musk made a purposeful choice for Grok to say what it did (i.e. inferring malice instead of negligence). For another example, people with this perspective may think that the left-wing bias of early LLMs was given to them explicitly by the companies that trained them — which it was not (to the best of my knowledge)[2].

Under this folk perspective, it also makes sense to try using LLMs to do relatively difficult arithmetic problems, believing that, since it is a “computer,” calculations must be easy for it. These days, ChatGPT will mostly get those calculations correct; however, in the past, ChatGPT would often get such questions wrong, especially if they required chaining multiple steps together. This is very surprising under the early folk-perspective. This would lead those holding this perspective to believe that there is some error with the way the models’ calculations work — or that the model was glitching in some way, as “computers should not get calculations wrong.” In truth, this is just a misunderstanding of the way that LLMs work.

The New Folk Perspective

Now that models are much better, there is a second folk perspective gaining traction, which leans much more heavily into anthropomorphism of LLMs. This perspective suggests that LLMs have desires, want to help, and are listening to you with rapt intention. I expect this folk perspective to grow over time, eventually likely outcompeting the early folk-perspective.

I also think this perspective is more defensible than the early folk-perspective in principle, but “misses the trees for the forest.” Moreover, this perspective — when taken to extremes — can lead to really bad outcomes, such as not checking work, and believing that the model’s advice is reliable and meaningful, similar to the advice you may be given by e.g. a human therapist, doctor, or friend.

This new folk-perspective is also much more “fragile” to learning information about how models actually work, similar to previous advances in AI. For example, a chess-playing AI initially seems like a computer that really understands chess. Someone without much background in AI may think this computer must be moderately intelligent. However, when you explain how it works in detail, e.g. by describing the Alpha-Beta pruning algorithm or Monte-Carlo Tree Search, people feel that the intelligence has been somehow deflated or explained away. I think this deflation — in a meaningful way — is less true for LLMs than it is for chess AIs, however people often react in a similar fashion when they learn how today’s models work, whether this is correct or not. They then tend to move to the next perspective in this list.

The next-token predictor perspective

The next-token predictor perspective is an incredibly common one, especially on X/Twitter, among academics (i.e. Bluesky), and with those who know slightly more about how LLMs actually work than than those who hold either of the folk-perspectives[3]. The crux is that LLMs — being deep-learning models which output a likelihood for what the next word is going to be — are simply “predicting the next likeliest token.” This perspective supposes that LLMs are essentially performing an interpolation from all the data they’ve seen so far to try and determine what the most probable next token is — and not much more than that.

Usually, the implication of this is that LLMs are therefore unable to come up with anything novel. This is also backed up by experience. LLMs are much better at programming in Python than in e.g. OCaml, as the latter is a much rarer programming language in data sources online. The data distribution does seem to have a large effect on how good the model can be at certain tasks — exactly what this theory would predict!

There are, however, a few issues with this thesis as it is usually stated: that models are literally, or even approximately just doing next-token prediction. This is certainly a significant part of the training process of modern LLMs, but it is absolutely not the whole story. This is what a model that is literally just doing next token prediction does when asked a question:

“We’re asking capital city questions!” The model thinks, “I know how to do this!” (The model output is everything after “Model output.” It kept going for a while)

This is, of course, very different from what we would expect from ChatGPT. That’s because LLMs get additional training beyond just how to “predict the next-token.” This training comes in three stages.

First, they need to understand what a “chat context” is, which requires: the distinction between user and assistant; the fact that there will be messages from a user and an assistant that (mostly) alternate; the fact that the LLM itself is the assistant in the exchanges. So they are trained on chat contexts until they understand this — this is done via a training method called “supervised fine-tuning”[4].

Second, they need to understand that they should be a “helpful, harmless, and honest” chat assistant. This is done via RLHF[5] (Reinforcement Learning from Human Feedback). By the end of this process, we get a model like the original ChatGPT (which came out in late November of 2022).

Third, we put the model through “reasoning” training[6]. This is a somewhat novel innovation (September 2024), and it works similarly to RLHF, but attempts to get the model to be “smarter” instead of more “helpful, harmless, and honest.” This is what causes modern LLMs to say that they’re “thinking,” before they respond.

Hopefully, you can see why I’m not that sympathetic to the “next token predictor” perspective on LLMs. It is true that the majority of the compute used to train LLMs does go into training them to get good at next token prediction (for now), as this generally upper bounds how good the model can get after the later stages of training — so this perspective is not entirely unreasonable. However, it’s missing any description of the innovations that have brought LLMs to the attention of the very people who tend to hold this perspective.

The next-token predictor perspective with a twist

There’s an alternative perspective that says that LLMs are actually mostly next-token predictors. However, this alternative perspective would say that the job of next-token prediction is actually incredibly difficult[7]! The fact that LLMs are able to predict next tokens as well as they do should astonish us, since the difficulty of being able to reliably predict data on the internet is highly non-trivial. Imagine, for example, that you were given a list of factorizable numbers, and then a list of their factors, as follows:

5744, 2, 2, 2, 2, 359 10201, 101, 101 ...

Predicting this text is going to be very difficult indeed, as it is believed that there is no polynomial-time algorithm that is able to factor numbers into their prime factors. Moreover, an LLM predicting this text would need to make its best guess in a single computation! That is, without the ability to “think carefully” before it outputs an answer.

This perspective claims that the type of intelligence which has gotten incredibly, marvelously good at next-token prediction is much more powerful than we would naively expect. This is not just because of “prime factorization” games like the one above (which certainly can be found on the internet). They also have some ability to model and predict the next word that e.g. Terence Tao is going to type on his blog. This indicates a high level of intelligence indeed. Even for the average Reddit poster, modelling them well enough to predict exactly what they’ll type next is not easy! This leads naturally to the next perspective.

The Simulator Perspective

This perspective posits that LLMs are not “doing language” similar to the way that humans do language[8], but are instead ‘mimicking’ language — analogously to how physics models mimic physics. A physics model will not get all the rules of fluid dynamics totally correct, but it will ensure that momentum is conserved, that energy is conserved, that fluids obey a rough approximation of the Navier-Stokes equation.

A language model, this perspective says, is similar. However, instead of conservation of momentum, the fundamental rules are more like:

• In English, subjects usually come before verbs.
• Speakers tend to continue to use similar vocabulary.
• Poems almost always have a rhyme structure.

Then LLMs can also, on this view, instantiate characters. Much in the same way that in principle a good-enough physics model could instantiate a human by accurately modelling what neurons would fire[9] , and what the human would proceed to say as a result. However, modelling characters is much easier for a language model, as the fundamental unit of reality for a language model is the linguistic token. Moreover, they need to be able to faithfully “simulate” characters in order to predict text effectively.

Then, when SFT (supervised fine-tuning) and RLHF (Reinforcement Learning from Human Feedback) are applied to the language model, the base model is molded into a simulation of a helpful AI assistant. Sometimes, this simulation decides to “go rogue” — based on previous examples of AI assistants going rogue (as occasionally, in the text they’ve been predicting, AI assistants go rogue, e.g. in science-fiction). So, this perspective says: the chatbot that you’re interacting with is a simulation of an AI assistant performed by a very alien “language simulator” that has no innate desires, wants, or needs[10]. This is captured well by the famous “Shoggoth meme”.

White-Box Perspectives

White-box perspectives are generally more mathematically flavored and require knowledge of the internals of a transformer. So, in order to understand these perspectives, it is necessary to know roughly what is going on inside a transformer. I will do my best to explain this quickly, but if you want more information, there are many good explainers elsewhere online. There are essentially three components of a transformer:

1. The embedding/unembedding: This converts words to vectors. That is, it takes a word[11] and converts it into a series of numbers so that the model can “do math on it,” since AI models are fundamentally numerical objects. Then at the end when we have our final output, we need to associate words to the final vector we produce, so we can generate the next token, and so the unembedding converts the vectors back into words.

2. The MLP layers: An MLP is a fully connected network. It is essentially the traditional view of what AI is, as presented in this image below:

3. The Attention layers: This was the big innovation that made the Transformer so successful. Attention layers basically allow information to be passed forwards and backwards. They are quite complicated to explain in detail — at a high level, if I give a Transformer the sentence:

   I saw a black swan and a grey goose. I fed the black…

   Then the attention layers allow the model to move the information “the thing being described is a swan” from the word swan to the first occurrence of the word “black”[12]. Then when the model encounters the word “black” again, it can pass that information forward, so that it knows the next token should be “swan.” This is incredibly useful for language modelling in general.

The mathematical framework perspective

The first perspective is the “mathematical framework” for Transformers that was established by Anthropic in their paper, which was published at the end of 2021. This view aimed to treat transformers as fundamentally “linear” models, with some annoying non-linearities added in. One important claim of this perspective is the importance of the residual stream. The residual stream is what all of the layers described above add their results to, and what the layers read from in order to perform their calculations. It’s not really a component of the transformer like the attention or MLP layers, it’s just how information moves from a previous layer to a future layer.

However, under this view, it’s one of the most important parts of the transformer — it is the “information highway,” along which all of the Transformers information and calculations get passed.

 

This view would state further that the layers of the Transformer, both the Multi-Layer Perceptron (MLP) layers and Attention layers are essentially performing “edits” to the residual stream in order to iteratively improve the model’s accuracy at predicting tokens, as you can see here:

 

However, the attention and MLP layers have different and complementary goals under this perspective. The MLP layers act as a “store of information,” so that if a model “remembers” a fact, such as “Paris is in France,” then this information will mostly lie somewhere in the weights of the MLP. The MLP also enables the model to do computations, so that if it needs to calculate the result of e.g. a mathematical expression, the actual calculation will mostly occur somewhere in an MLP (or distributed across multiple MLP layers). The attention layers then allow the model to pass information between different tokens as I described earlier, which also includes all the computations that an MLP may have done.

The naive version of this perspective was a hopeful one! It claimed that Transformers are, for the most part, a composition of a bunch of linear operations. Linear operations are generally not too difficult to understand and disentangle. So long as everything is represented linearly, we’ll be able to understand what’s going on inside a Transformer — sure, there were some issues with nonlinearities: the activation function[13], LayerNorm[14] — but those are just details.

It soon became clear there was a bigger issue.

The Superposition Perspective

Superposition is when models have to represent more things than they have dimensions or neurons[15]. This means that dimensions can’t correspond easily to “things the model is doing,” and poses a major challenge for interpreting what the model is doing. There are two types of superposition — “bottleneck superposition,” and “neuron superposition.”

Bottleneck superposition is intuitively not too difficult to understand. If there are 50,000 tokens in your vocabulary, but only 1000 dimensions, then it can’t be that each token is assigned its own dimension — there must be some “interference” between the embeddings of different tokens, just for storage. However, this issue is not too difficult to address. We just need to do the work of disentangling where different tokens — and information about these tokens — gets stored. This is doable.

The more difficult superposition is “neuron superposition.” This is when neurons (mostly in MLPs —though it has been observed in attention layers as well), actually do their computations in a distributed way[16]. This means that even if we managed to solve the issues with bottleneck superposition — doable, but certainly not an easy task by any means— we would still end up in a situation where we’re not sure how the model actually uses these concepts to compute things, since the computations are also all happening in superposition, and involve their own non-linearities.

Solving this issue has been the central organizing problem that those trying to understand Transformers have tried to address over the past three years. Progress has been made, and we’re definitely in a better place when it comes to understanding Transformers than we were, but it turns out that addressing superposition is much more difficult than we’d originally thought when the mathematical perspective was first established.

The Energy Minimization perspective

The final perspective on Transformers I’ll describe is a perspective on how they are trained, and how they get such impressive capabilities. This view departs strongly from the “next-token prediction” view of transformers, in favor of trying to explain both how they are so good at next-token prediction, and how they are good enough at generalizing to solve never-before-seen IMO problems.

Classically, in machine-learning, we are just trying to minimize our training objective — often called the “loss[17].” For Transformers during pre-training, this loss function is basically “How well did you predict what the next token would be?” During RLHF it would be “How well did your response comport to Human Values[18]”

The “energy minimization perspective” says that something else is going on too. Due to a combination of: gradient descent; the structure of our loss function; and the fact that there are symmetries within transformers. It claims that we’re implicitly also training with a “simplicity” prior. This means that early in training, the model focuses on minimizing loss by manually learning the rules of how to predict tokens. However, later in training, the main thing that affects the model’s learning is how “simple” or “generalizable” the model’s algorithm for predicting the next token is. This causes models to have a bias towards simple algorithms for predicting the next token — this enables for much more capacity to generalize[19] than we would naively expect under the “predict next token” framework.

This is called the “energy minimization perspective” because in Bayesian learning theory, what causes models to reach more simple and generalizable solutions is the fact that they are minimizing a quantity called the “free energy” of the system[20]. It has been proved that we can represent the free energy as basically a “loss minimization” term and a “simplicity” term (in the limit). The free energy perspective says that to really understand a transformer, we need to understand the effects of this simplicity term, as this is what allows them to be so powerful and generalize so effectively as we increase the amount of data we show them[21]. This perspective has spurred a lot of work in singular learning theory as applied to modern AI models.

Conclusion

This has been a pretty long post by my standards, so to conclude I’ll just give my takes on what perspectives I think are true. I think the simulator perspective, the superposition perspective, and the free energy perspective are basically true. The rest of them I think are either oversimplified (the mathematical perspective — though it was great for the time — and the next-token predictor perspectives) or just plain wrong (the folk-perspectives).

However, you don’t need to agree with me! I hope this post has left you in a more informed position to make up your own mind.

1. ^

   I’m hoping for this post to be a pretty accessible description of the major current perspectives on transformers. So I’ll warn that I’m going to elide some of the details of current training processes (which are actually incredibly complex nowadays) as well as, in the later section, eliding some of the mathematical detail. I’ll try and provide links to more information wherever possible.

2. ^

   Though they also probably didn’t work too hard to prevent it. But it wasn’t a conscious choice in the way that this perspective often posits.

3. ^

   Basically this:

4. ^

   The actual mechanics of how supervised fine-tuning works, especially in the chat context: We make sure during all of training there are some special tokens that are never encountered in pre-training. These tokens designate things like “This is a user message,” “This is an assistant message,” there are others, but let’s focus on the minimal example.

   Then after the model has learnt how to predict text on the internet effectively, we give it a bunch of examples of “Chat histories” that involve these tokens and clarify to the model that it is the assistant. So, in this phase of training, the model never learns to predict the user’s message, it is trained only to predict the things that a chat assistant would say.

   This training essentially works the same as pre-training, although during pre-training — because we do so much of it — we only run the model on the scraped internet once, since there are diminishing returns to doing it twice. The chat training examples are much smaller, so we can run the model on it multiple times, and often do. By the end of it, the model will understand that it should be acting the role of an assistant.

5. ^

   There’s other methods, but they’re generally conceptually quite similar to RLHF.

   RLHF works as follows: we ask a lot of humans to provide preferences for which of the responses are better (via A/B comparisons). Then we can infer an “ordering” about which responses are better, and train a different model to predict how high a given response would come in that ordering.

   We then get the LLM to generate a bunch of responses (since it now understands the chat context it should be in), and train it to increase the likelihood that a human would say “Yes, this a good response” and decrease the likelihood of “No, this is a bad response.”

6. ^

   This gets the model to think for longer, consider its response, and try and weigh all the possible choices until finally the model outputs an answer that is hopefully more accurate than if it didn’t think.

   It works by getting the model to output a large number of tokens before answering, and models naturally will use those tokens to help them come up with their final answer. If their final answer to a question —usually a programming or mathematics problem — is correct, then we encourage those thought patterns. If it gets the question wrong, we discourage those thought patterns.

7. ^

   You can try it yourself here.

8. ^

   I.e. to communicate inner feelings, desires, wants.

9. ^

   Ignoring debates about physicalism.

10. ^

    However, the character that they create may have wants, desires, and needs. Much the same way as if we simulated a human in a physics model, they could have wants, desires, and needs, and be moral patients.

11. ^

    Okay a word is not necessarily a token and a token is not necessarily a word, but that is just unnecessary details for the most part. If, whenever you hear “token” you think “word,” you will be 95% correct.

12. ^

    I.e. so the model knows that the adjective “black” is modifying the word “swan.”

13. ^

    Activation functions are explained well here. They basically stop the model from being one big matrix multiplication.

14. ^

    LayerNorm is explained relatively well here, don’t worry about it too much though. It’s not that necessary to understand what Transformers are doing. We need LayerNorm for arcane training reasons, and in fact, we can remove it if we’re careful.

15. ^

    I know it sounds like some quantum woo thing. It’s not, they just chose superposition because you can never be certain which feature a certain vector corresponds to.

16. ^

    I know this is vague, but I really cannot go into more detail about this here. It would take very long to explain. There’s lots of good information about “computation in superposition” online though!

17. ^

    Is this loss?

18. ^

    By which, of course, we mean the values of workers paid below-minimum-wage to trawl through horrific model outputs somewhere in the Global South.

19. ^

    Since simple algorithms generalize better. This has been generally observed. It’s basically Occam’s razor.

20. ^

    The free energy is:

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    where n nis the number of datapoints, we integrate over all possible parameters, Ln  is the loss function for the weights, and φ(w) is the prior probability of parameters — but don’t worry about it too much.

21. ^

    It says a lot of other things too, but much like the free-energy people, I’m going to approximate to first-order!

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